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From Weakly Supervised Learning to Biquality Learning: An Introduction


Abstract and Figures (this paper has been accepted at IJCNN 2021) The field of Weakly Supervised Learning (WSL) has recently seen a surge of popularity, with numerous papers addressing different types of "supervision deficiencies". In WSL use cases, a variety of situations exists where the collected "information" is imperfect. The paradigm of WSL attempts to list and cover these problems with associated solutions. In this paper, we review the research progress on WSL with the aim to make it as a brief introduction to this field. We present the three axis of WSL cube and an overview of most of all the elements of their facets. We propose three measurable quantities that acts as coordinates in the previously defined cube namely: Quality, Adaptability and Quantity of information. Thus we suggest that Biquality Learning framework can be defined as a plan of the WSL cube and propose to re-discover previously unrelated patches in WSL literature as a unified Biquality Learning literature.
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From Weakly Supervised Learning to Biquality
Learning: an Introduction
Pierre Nodet
Orange Labs
AgroParisTech, INRAe
46 av. de la R´
atillon, France
Vincent Lemaire
Orange Labs
2 av. P. Marzin
Lannion, France
Alexis Bondu
Orange Labs
46 av. de la R´
atillon, France
Antoine Cornu´
AgroParisTech, INRAe
e Paris-Saclay
16 r. Claude Bernard
Paris, France
Adam Ouorou
Orange Labs
46 av. de la R´
atillon, France
Abstract—The field of Weakly Supervised Learning (WSL)
has recently seen a surge of popularity, with numerous papers
addressing different types of “supervision deficiencies”. In WSL
use cases, a variety of situations exists where the collected
“information” is imperfect. The paradigm of WSL attempts to
list and cover these problems with associated solutions. In this
paper, we review the research progress on WSL with the aim
to make it as a brief introduction to this field. We present the
three axis of WSL cube and an overview of most of all the
elements of their facets. We propose three measurable quantities
that acts as coordinates in the previously defined cube namely:
Quality, Adaptability and Quantity of information. Thus we
suggest that Biquality Learning framework can be defined as
a plan of the WSL cube and propose to re-discover previously
unrelated patches in WSL literature as a unified Biquality
Learning literature.
Index Terms—weakly, supervised, classification, prediction,
noisy labels, trusted and untrusted data, ...
In the field of machine learning, the task of classification
can be performed by different approaches depending on the
level of supervision of training data. As shown in Figure 1,
unsupervised, weakly supervised and supervised approaches
form a continuum of possible situations, starting from the
absence of ground truth and ending with complete and perfect
ground truth. For the most part, the accuracy of the models
learned increases as the level of supervision of data increases.
Additionally, the level of supervision of a dataset can be
increased in return for a labelling cost. In [1], the authors
indicate that an interesting goal could be to obtain a high
accuracy while spending a low labeling, cost.
In Weakly Supervised Learning (WSL) use cases (e.g. fraud
detection) a variety of situations exist where the collected
ground truth is imperfect. In this context, the collected labels
may suffer from bad quality, non-adaptability (defined in Sec-
tion IV) or even insufficient quantity. For instance, automatic
labeling system could be used without any real guarantee that
the data is complete, exhaustive and trustworthy. Alternatively,
manual labelling is also problematic in practice as obtaining
labels from an expert is costly and the availability of experts
is often limited. Consequently, there are many real-life situa-
tions where imperfect ground truth must be used because of
Fig. 1. Classification of Classification from [1]
some practical considerations such as cost optimization, expert
availability, difficulty to certainly choose each label.
This general problem of supervision deficiency has attracted
a recent focus in the literature. The paradigm of Weakly
Supervised Learning attempts to list and cover these problems
with associated solutions. The work of Zhou in [2] is a first
successful effort to synthesise this domain. In this paper, the
objective is threefold: (i) to suggest another view of WSL, (ii)
to propose a larger and updated taxonomy compared to [2],
and then (iii) to highlight a new emergent view of a part of
the WSL, namely the biquality learning.
The rest of this paper is organized as follows. In Sec-
tion II, we present the three axis of the Weakly supervised
Learning cube and an overview of most of all the elements
of their facets. Section III gives additional elements which
have to be taken in consideration at the crossroad of these
three axes or when dealing with Weakly learning problems.
Section IV suggests 3 key concepts which help at summarizing
WSL: Quantity, Quality and Adaptability. In Section V, these
3 concepts are used to raise links between some learning
frameworks jointly used in WSL as in Biquality Learning.
Section VI then give existing works examples of Biquality
Learning. Finally the Section VII concludes this paper.
The taxonomy proposed in this paper is organised in the
form of a “cube” and is presented in Figure 2. This section
arXiv:2012.09632v3 [cs.LG] 23 Apr 2021
Fig. 2. Taxonomy: an attempt - The big picture
progressively presents the differences between weakly super-
vised approaches by going through the axes of this cube.
First of all, a distinction must be made between strong
and weak supervision. On the one hand, strong supervision
correspond to the regular case in machine learning where the
training examples are expected to be exhaustively labelled with
true labels, i.e. without any kind of corruption or deficiency.
On the other hand, weak supervision means that the available
ground truth is imperfect, or even corrupted. The WSL field
aims to address a variety of supervision deficiencies which
can be categorized in a “cube” along the following three axes
as illustrated on Figure 2: inaccurate labels (Axis 1), inexact
labels (Axis 2), incomplete labels (Axis 3).
These three axes are detailed in the rest of this section and
constitute the proposed taxonomy. The reader may note that
the boundaries between these axes are not hard: i.e a part could
be moved from an axis to another or belong to two axes, this
is a suggestion.
A. Axis 1: Inaccurate Supervision - True Labels vs. Inaccurate
Lack of confidence in data sources is a frequent issue when
it comes to real-life use cases. The values used as learning
targets, also called labels or classes, can be incorrect due to
many factors.
In practice, a variety of situations can lead to inaccurate
labels: (i) a label can be assigned to a “bag of examples ” such
as a bunch of keys. In this case, at least one of the examples
in the keychain actually belongs to the class indicated by
the label. Multi-instance learning [3]–[6] is an appropriate
technique to deal with this type of of learning task. (ii) a label
may not be “guaranteed” and may be noisy. In theory, the
learning set should be labeled in a way that is unbiased with
respect to the concept to be learned. However, the data used
in real-world applications provide an imperfect ground truth
that does not match the concept to be learned. As defined in
[7], noise is “anything that obscures the relationship between
the features of an instance and its class”. According to this
definition, every error or imprecision in a attribute or label is
considered as noise, including human deficiency. Noise is not
a trivial issue because the origin is never clearly obvious. In
practical cases, this leads to troubles into evaluating existence
and strength level of noise into a dataset. Frenay et al. in [8]
provide a good overview of noise sources, impact of labeling
noise, types of noise and dependency to noise. Below is a
non-exhaustive list of common ways to learn a model in the
presence of labeling noise1:
in case of mariginal noise level, a standard learning
algorithm that is natively robust to label noise, is used
use a loss function which solves theoretically (or em-
pirically) the problem in case of (i) noise completely at
random2[13]; or (ii) class dependent noise [14], [15].
In most cases, this type of approach is known in the
literature as “Robust Learning to Label noise (RLL)”;
model noise to assess the quality of each label (requires
assumptions on noise) [16];
enforce consistency between the model’s predictions and
the proxy labels [17];
clean the training set by filtering noisy examples [18]–
trust a subset of data provided by the user, in order to
learn a model at once on trusted examples (without label
noise) and untrusted ones [14], [23], [24].
Another kind of ”noise” appears when each training exam-
ple is not equipped with a single true label but with a set of
candidate labels that contains the true label. To deal with this
kind of training examples, Partial Label Learning (PLL) has
been proposed [25] (also called ambiguously labeled learning).
It has attracted attention as for example in the algorithms IPAL
[26], PL-KNN [25], CLPL [27] and PL-SVM [28] or when
suggesting semi-supervised partial label learning as in [29].
This setting is motivated, for example, by common scenario in
many image and video collections, where only partial access
to labels is available. The goal is to learn a classifier that
can disambiguate the partially-labeled training instances, and
generalize to unseen data [30].
B. Axis 2: Inexact Supervision - Labels at the Right Proxy vs.
not at the Right Proxy
The second axis describes inexact labeling which is orthog-
onal to the first type of supervision deficiency - i.e. inexact
labeling and noisy labeling may coexist. Here, the labels are
provided not at the right proxy, which corresponds to one (or
possibly a mixture) of the following situations:
Proxy domain: the target domain differs between the
training set and the test set. For instance, it could be
learning to discriminate “panthers” from other savanna
animals based one “cats” and “dogs” labels. Two cases
can be distinguished: (i) training labels are available in
another target domain than test labels (ii) or training
labels are available in a sub-domain that belongs to the
original target domain. Domain transfer [31] or domain
adaptation [32] are clearly suitable techniques to address
these learning tasks.
Proxy labels: some unlabeled examples are automatically
labeled, either by a rule-based system or by a learned
model, in order to increase the size of the training set.
1Note: the number of articles published on this topic has exploded in recent
2defined in Section IV-B.
This kind of labels are called proxy labels and can be
considered as coming from a proxy concept close to
the one to be learned. Only the true labels stand for
the ground truth. The way proxy labels are used varies
depending on their origin. In the case where proxy labels
are provided by the classifier itself without any additional
supervision, the self-training (ST) [33], the co-training
(CT) and their variants attempt to improve the learned
model by including proxy-labels in the training set as
regular labels. Other approaches exploits the confidence
level of the classifier to produce soft-proxy-labels, and
then exploit it as weighted training examples [34]. In
the case where proxy labels are generated by a rule-
based system, the quality of labels depends on the experts
knowledge which is manually inputted into the rules.
Ultimately, a classifier learned from such labels can be
considered as a means of smoothing the set of rules,
allowing the end-user to score any new example. Some
recent automatic labeling system offer an intermediate
way that mixes rule-based systems and machine learning
approaches (MIX) [35], [36].
Proxy individuals: the statistical individuals are not
equally defined between the training set and the test
set. For instance, it could be learning to classify images
based one labels that only describe a parts of the images.
Multi-instance learning (MIL) is an other example which
consists in learning from labeled groups of individuals.
In the literature, many algorithms have been adapted to
work within this paradigm [3]–[6].
C. Axis 3: Incomplete Supervision - Few labels vs. Numerous
The third axis describes incomplete supervision which con-
sists of processing a partially labeled training set. In this
situation, labeled and unlabeled examples coexist within the
training set, and it is assumed that there are not enough labeled
examples to train a performing classifier. The objective is to
use the entire training set, including the unlabeled examples,
to achieve better classification performance than learning a
classifier only from labeled examples.
In the literature, many techniques exist capable of process-
ing partially labeled training data, i.e. active learning (AL),
semi-supervised learning (SSL), positive unlabeled learning
(PUL) or self-training (ST) and Co-Training (CT). At the
bottom of the Figure 2, we suggest to sort these methods
according to the quantity of labeled examples they require.
All these approaches are detailed below.
1) Active Learning (AL) [37]: Modern supervised learning
approaches are known to require large amounts of training
examples in order to achieve their best performance. These
examples are mainly obtained by human experts who label
them manually, making the labelling process costly in practice.
Active learning (AL) is a field that includes all the selection
strategies that allow one to iteratively build the training set
of a model in interaction with a human expert (also called
oracle). The aim is to select the most informative examples to
minimize the labelling cost.
Active learning is an iterative process that continues until
a labelling budget is exhausted or a predefined performance
threshold is reached. Each iteration begins with the selection
of the most informative example. This selection is generally
based on information collected during previous iterations (pre-
dictions of a classifier, density measures, etc.). The selected
example is then submitted to the oracle which returns the
associated class, and the example is added to the training set
(L). The new learning set is then used to improve the model
and the new predictions are used to perform the next iteration.
In conventional heuristic the utility measures used by active
learning strategies [37] differ in their positioning with respect
to the trade off between exploiting the current classifier and
exploring training data. Selecting an unlabelled example in an
unknown region of the observation space helps to explore the
data, so as to limit the risk of learning a hypothesis that is too
specific to the set Lof currently labeled examples. Conversely,
selecting an example in an already sampled region allows to
locally refine the predictive model. We do not intend to provide
an exhaustive overview of existing AL strategies and refer to
[37], [38] for a detailed overview, [39]–[41] for some recent
benchmark and a new way to treat uncertainty in [42]
Another meta active learning paradigm exists, which com-
bines conventional strategies using bandit algorithms [43]–
[48]. These meta-learning algorithms intend to select online
the best AL strategy according to the observed improvements
of the classifier. These algorithms are capable of adapting their
choice over time as the classifier improves. However, learning
must be done using few examples to be useful and these kind
of algorithms suffer from the cold start problem. In addition
these approaches are limited to combine existing AL heuristic
Other meta-active-learning algorithms have been developed
to learn an AL strategy starting from scratch, using multiple
source of datasets. These algorithms are used by transferring
the learned AL strategy to new target datasets [49]–[51]. Most
of them are based on modern reinforcement learning methods.
The major challenge is to learn an AL strategy general enough
to automatically control the exploitation/exploration trade-off
when used on new unlabeled datasets (which is impossible
using heuristic strategies). A recent evaluation of learning
active learning can be found in [52].
2) Semi Supervised Learning (SSL): Early work on semi-
supervised learning dates back to the 2000s, an overview of
these pioneering papers can be found in [53]–[57]. In the
literature, the SSL approaches can be categorized into two
Algorithms that use unlabeled examples unchanged. In
this case, the unlabeled examples are treated as un-
supervised information added to the labeled examples.
Four main categories exist: generative methods, graph-
based methods, low-density separation methods, and
disagreement-based methods [2].
Semi-supervised learning algorithms that produce proxy
labels on unlabeled examples, which are used as targets
in addition to the labeled examples. These proxy labels
are produced by the model itself or by its variantswithout
any additional supervision. They are not strictly ground
truth, but may nevertheless be useful for learning. At
the end, these inaccurate labels (see Section II-A) can
be considered as noisy. The rest of this section deals
with particular cases of SSL and presents the Postive
Unlabeled Learning , the Self Training and the Co-
training approaches.
3) Postive Unlabeled Learning (PUL): Learning from Pos-
itive and Unlabeled examples (PUL) is a special case of
binary classification and SSL [58]. In this particular setting,
the unlabeled examples may contain both positive and negative
examples with hidden labels. These approaches differ from
a one-class classification [59] since they explicitly use the
unlabeled examples in the learning process. In the literature,
the PUL approches can be divided into three groups: i) the
two-step techniques, (ii) the biased learning and (iii) the class
prior incorporation techniques.
The two-step techniques [60] consists in: (1) identifying
reliable negative examples and optionally generating addi-
tional positive examples [61]; (2) using supervised or semi-
supervised learning approaches which process the positively
labeled examples, the reliable negatives examples, and the
remaining unlabeled examples; (3) (when applicable) select-
ing the best classifier generated in Step 2. Biased learning
approaches consider PU data as fully labeled examples with
noisy negative labels. At last, class prior incorporation ap-
proaches modify standard learning algorithms by applying the
mathematics from the SCAR setup (see Section III-B).
4) Self Training (ST): Self-training has not a clear defi-
nition is the literature, it can be viewed as a “single-view
weakly supervised algorithm”. First a classifier is trained
from the available labeled examples and then this classifier
is used to make predictions and build new proxy labels. Only
those examples where confidence in proxy labelling exceeds
a certain threshold are added to the training set. Then, the
classifier is retrained from the training set enriched with proxy-
labels. This process is repeated in an iterative way [33].
5) Co-Training (CT) [62]–[65]: Starting from a set of par-
tially labeled examples, co-learning algorithms aim to increase
the amount of labeled examples by generating proxy-labels.
Co-training algorithms work by training several classifiers
from the initial labeled examples. Then, these classifier are
used to make predictions and generate proxy-labels on the
unlabeled examples. The most confident predictions on these
proxy-labels are then added to the set of labeled data for the
next iteration.
One important aspect of co-training is the relationship
between the views (the sets of explicative variables) used in
learning the different models. The original co-training algo-
rithm [62] states that the independence of the views is required
to properly perform automatic labeling. More recent works
[66]–[68] show that this assumption can be relaxed. Another
requirement is to obtain at the iteration step a “reasonable”
classifier in terms of performances , this explains why we
place co-training on the left of AL and SSL in Figure IV-A.
In [69], a study is given on the optimal selection of the co-
training parameters.
Co-training can also be considered as a member of ”multi-
view training” family in which some other members belong to,
such as: Democratic Co-learning [70], Tri-training [71], Asym-
metric tri-training [72], Multi-task tri-training [73], which are
not described here.
A. Learning at the crossroad of the three axis
The use of a cube to describe the literature on Weakly
Supervised Learning allows us not only to use the axes, but
also the volume of the cube to position existing approaches.
It is now easy to position more subtly the approaches that
are related to several axes at once. For example, Partial Label
Learning may be related to two supervision deficiencies: i)
inexact supervision, because multiple labels are provided for
each training example; ii) inaccurate supervision, because only
one of the labels provided is correct. Positioning the PLL on
the plane defined by these two axes seems more relevant.
Also, this representation allows to highlight some interesting
intersections, between two axes or between an axis and a
plane. One of these points of interest is the origin of the
three axes, which corresponds to the case where supervision
is absolutely inaccurate, imprecise and incomplete, which
ultimately amounts to unsupervised learning. Similarly, the
point at the opposite end of the cube corresponds to perfectly
precise, accurate and complete supervision, which equates to
supervised learning.
Finally this representation could provide insights on the
reasons of using proven techniques from a particular subfield
of Weakly Supervised Learning can be efficient in another
one. For instance, DivideMix [74] chooses to reuse the effi-
cient MixUp [75] approach from Semi-Supervised Learning
to tackle the problem of Learning with Label Noise. This
approach uses Data Augmentation [76] and Model Agreement
[77] to estimate labels probabilities and then discard or keep
provided labels.
This section is not exhaustive, interested readers will be
able to position the approaches of the literature in the cube
B. Deficiency Model
The deficiency model describes the nature of the supervision
deficiency. It is usually described as a probability measure
called ρ: (x, y)7→ ρ(x, y), indicating if an example is corrupt
or not. ρcan depends on the value the explanatory variables
x X , the label value y∈ Y or both (x, y ). The different
types of supervision deficiency described in this section are the
following: (i) Completely At Random (CAR), (ii) At Random
(AR) and (iii) Not At Random (NAR).
If the probability of being corrupted is the same for all
training examples, ρ: (x, y)7→ ρc,ρc[0,1], then the su-
pervision deficiency model is Completely At Random (CAR).
This implies that the cause of the supervision deficiency data
is unrelated to the data. If the probability of being corrupted
is the same within classes, ρ: (x, y)7→ ρy,y∈ Y,
ρy[0,1], then the supervision deficiency model is At
Random (AR). If neither CAR nor AR holds, then we speak
of Not at Random (NAR) model. Here the probability of being
corrupted is dependent on both the samples and the label
value, ρ: (x, y)7→ ρ(x, y). These three deficiency models can
be ranked in a descendent manner, having the NAR model
being the most complex as it depends on both the instance
and label value, which requires a function to model, to CAR
model where only one constant is enough to describe it. These
models may help practitioner to find links between supervision
deficiencies. For example PUL is SSL with only one class
labeled, which means that the missingness of the label is linked
to the label value, so PUL is an extreme case of SSL AR with
ρ0= 1eand ρ1=e(where eis called the propensity score).
AL is another form of SSL where examples are labeled
thanks to a strategy, previously labeled instances and the or-
dered iterative process leading to non-iid labeled data. As such
AL is part of the SSL NAR family. We want to reiterate the
deficiency model can be applied to any supervision deficiency,
even if it has been mostly featured in RLL and in SSL.
C. Transductive learning vs. Inductive Learning
As we consider WSL framework, one may be tempted to use
the test set to guide the choice of the model. But in this case we
need to carefully decide if in the future the need of a model to
predict on another test (deployment) dataset is required or not:
two point of view could be considered transductive learning
vs. inductive learning, that is why now we add a note on them.
Training a machine could take many forms as supervised
learning, unsupervised learning, active learning, online learn-
ing, etc. The number of members is the family is large and
new members appear regularly as for example “federative
learning”. However one may establish a separation between
two constant classes based on the way the user would like to
use the “learning machine” at the deployment stage. The user
does not want necessarily a predictive model for subsequent
use on new data. Because, for example, it has the completeness
of the data for the problem to be treated. It is therefore neces-
sary to distinguish between inductive learning and transductive
On one side the goal of inductive learning is, essentially,
to learn a function (a model) which will be later used on
new data to predict classes (classification) or numerical values
(regression). The predictions may be seen as “buy-products”
of the model. Induction is reasoning from observed training
cases to general rules, which are then applied to the test
cases. On the other side the goal of transductive learning the
goal is not to obtain a function or a model but only to do
predictions on a given test database, and on only on this test
of instances. Transduction was introduced by Vladimir Vapnik
in the 1990s, motivated by the intuition that transduction is
preferable to induction since, induction requires solving a
more general problem (inferring a function) before solving
a more specific problem (computing outputs for new cases).
However the distinction between inductive and transductive
learning could be a hazy border for example in case of
semi-supervised learning. Knowing this, the view of Zhou in
[2] about “pure semi supervised learning” and transductive
learning is interesting. The distinction about Transductive
learning vs. Inductive Learning concerned most of the learning
form included on Figure 2.
Until now we see that many forms of learning and weakness
are intertwined. A way to resume their aspect was given
on Figure 2. From this point of view one may identified 3
common concepts that are described now.
A. Quantity |L|
Insufficient quantity of labels or training examples occurs
when many training examples are available but only a small
portion is labeled, e.g. due to the cost of labelling. For
instance, this occurs in the field of cyber security where
human forensics is needed to tag attacks. Usually, this issue is
addressed by few shot learning (FSL), active learning (AL)
[37] semi-supervised learning (SSL) [55] , Self Training,
or Co-Training or active learning (AL) which have been
described briefly above in this paper. Another way to see the
”quantity” could be the ratio between the number of examples
labeled and unlabeled (p).
B. Quality q
In this case, all training examples are labeled but the labels
may be corrupted. This usually happens when outsourcing
labeling to crowd labeling [78]. The Robust Learning to Label
Noise (RLL) approaches tackle this problem [79], with three
types of label noise identified: i) the completely at random
noise corresponds to a uniform probability of label change ;
ii) the class dependent label noise when the probability of label
change depends upon each class, with uniform label changes
within each class ; iii) the instance dependent label noise is
when the probability of label change varies over the input
space of the classifier. This last type of label noise is the most
difficult to deal with, and typically requires making sometimes
strong assumptions on the data.
C. Adaptability a
This is the case for instance, in Multi Instance Learning
(MIL) [3]–[6], in which there is one label for each bag of
training examples, and each example has an uncertain label.
Some scenarios in Transfer Learning (TL) [80] imply that
only the labels in the source domain are provided while
the target domain labels are not. Often, these non-adapted
labels are associated with the existence of slightly different
learning tasks (e.g. more precise and numerous classes are
dividing the original categories). Alternatively, non-adapted
labels may characterize a differing statistical individual [81]
(e.g. a subpart of an image instead of the entire image).
All the types of supervision deficiencies presented above
are addressed separately in the literature, leading to highly
specialized approaches. In practice, it is very difficult to
identify the type(s) of deficiencies with which a real dataset is
associated. For this reason, it would be very useful to suggest
another point of view as a tentative of an unified framework
for (a part of the) Weakly Supervised Learning, in order to
design generic approaches capable of dealing not a single type
of supervision deficiency. This is the purpose of this section
mainly given for cases where data are adapted to the task to
learn (a= 1).
Learning using biquality data has recently been put forward
in [14], [82], [83] and consists in learning a classifier from
two distinct training sets, one trusted and the other not. The
initial motivation was to unify semi-supervised and robust
learning through a combination of the two. We show in this
paper that this scenario is not limited to this unification and
that it can cover a larger range of supervision deficiencies as
demonstrated with the algorithms we suggest and their results.
The trusted dataset DTconsists of pairs of labeled examples
(xi, yi) where all labels yi∈ Y are supposed to be cor-
rect according to the true underlying conditional distribution
PT(Y|X). In the untrusted dataset DU, examples ximay
be associated with incorrect labels. We note PU(Y|X)the
corresponding conditional distribution.
At this stage, no assumption is made about the nature of the
supervision deficiencies which could be of any type including
label noise, missing labels, concept drift, non-adapted labels ...
and more generally a mixture of these supervision deficiencies.
The difficulty of a learning task performed on biquality
data can be characterised by two quantities. First, the ratio
of trusted data over the whole data set, denoted by p:
Second, a measure of the quality, denoted by q, which
evaluates the usefulness of the untrusted data DUto learn
the trusted concept For example in [83] qis defined using a
ratio of Kullback-Leibler divergence between PT(Y|X)and
SSL New range of problems
Fig. 3. The different learning tasks covered by the biquality setting, repre-
sented on a 2D representation.
The biquality setting covers a wide range of learning tasks
by varying the quantities qand p, as represented in Figure 3.
When (p= 1 OR q= 1) all examples can be trusted. This
setting corresponds to a standard supervised learning
(SL) task.
When (p= 0 AND q= 0), there is no trusted examples
and the untrusted labels are not informative. We are
left with only the inputs {xi}1imas in unsupervised
learning (UL).
On the vertical axis defined by q= 0, except for the
two points (p, q) = (0,0) and (p, q) = (1,0), the
untrusted labels are not informative, and trusted examples
are available. The learning task becomes semi-supervised
learning (SSL) with the untrusted examples as unlabeled
and the trusted as labeled.
An upward move on this vertical axis, from a point
(p, q)=(, 0) characterized by a low proportion of
labeled examples p=, to a point (p0,0), with p0> p,
corresponds to Active Learning, if an oracle can be
called on unlabeled examples. The same upward move
can also be realized in Self-training and Co-training,
where unlabeled training examples are labeled using the
predictions of the current classifier(s).
On the horizontal axis defined by p= 0, except for the
points (p, q) = (0,0) and (p, q) = (0,1), only untrusted
examples are provided, which corresponds to the range of
learning tasks typically addressed by Robust Learning
to Label noise (RLL) approaches.
Only the edges of Figure 3 have been envisioned in previous
works – i.e. the points mentioned above – and a whole new
range of problems corresponding to the entire plan of the figure
remains to be explored. Biquality learning may also be used
to tackle particular tasks belonging to WSL, for instance:
Positive Unlabeled Learning (PUL) [58] where the trusted
examples are only positive and untrusted examples those
from the unknown class.
Self Training and Co-training [62]–[64] could be adressed
at the end of their self labeled process: the initial training
set is the trusted dataset, all examples labeled after (dur-
ing the self labeling process) are the untrusted examples.
Concept drift [84]: when a concept drift occurs, all the
examples used before a drift detection may be considered
as the untrusted examples, while the examples available
after it are viewed as the trusted ones, assuming a perfect
labeling process.
Self Supervised Learning system as Snorkel [35]: the
small initial training set is the trusted dataset, all exam-
ples automatically labeled using the labeling functions
correspond to the untrusted examples.
As can be seen from the above list, the Biquality framework
is quite general and its investigation seems a promising avenue
to unify different aspects of the Weakly Supervised Learning.
In the previous section we have been describing how Wealky
Supervised Learning subfields fitted in the Biquality Learning
setup. Here we would be reviewing three of these subfields and
highlight prexisting Biquality Learning algorithms that either
have been made for a different purpose but still could be used
for WSL, or have been design directly for this setup.
A. Transfer Learning
Transfer Learning focuses on storing, knowledge gained
while solving one problem and applying it to a different
but related problem. Two datasets are at disposal, a source
dataset DSand target dataset DTthat are related to a source
domain DS(XS,P(XS)) and a target domain DT(XT,P(XT))
to solve the target task TT(YT,P(YT|XT)) with the help of
the source task TS(YS,P(YS|XS)). We can draw a parallel
between Biquality Learning notations and Transfer Learning
notations mostly by substituting (source, S) by (untrusted, U)
and (target, T) by (trusted, T).
A lot of different setups can derive from the general Transfer
Learning setup as Domain Adaptation, Transductive Transfer
Learning, Covariate Shift, ... Inductive Transfer Learning is
the setup closest to Biquality Learning, indeed most of the key
assumptions are the same : XT=XU,YT=YU,P(XT) =
For example, TrAdaBoost [85] is an extension of boosting
to Inductive Transfer Learning. TrAdaBoost learns on both
trusted and untrusted data every iterations. It behaves exactly
as AdaBoost [86], [87] on trusted data : mispredicted trusted
samples get more attention, but opposite on untrusted data :
mispredicted untrusted samples are ditched out.
Multi Task Learning [88] is another Inductive Transfer
Learning approach that improves generalization by learning
both tasks in parallel while using a shared representation; what
is learned for the untrusted task can help the trusted task. This
loss LMTL is usually defined by a convex combination of the
trusted loss LTand untrusted loss LUof the model f(with
LMTL(f(X), Y ) = (1 λ)LU(f(X), Y ) + λLT(f(X), Y )(2)
In Inductive Transfer Learning as in Transfer Learning in
general, we assume that the source task (i.e. untrusted task) is
relevant for the target task (i.e. trusted task). Nonetheless in
the Biquality Data setup, we can have the untrusted task that
bring no information to the trusted task, even bring adversarial
information. Thus using Inductive Transfer Learning algorithm
directly on Biquality Data setup can lead to bad predictive
For example, with Multi Task Learning, the global loss term
would be heavily perturbed as the untrusted loss could never
be optimized. For TrAdaBoost, the first model learned on both
trusted and untrusted samples would not be able to learn the
class boundaries correctly, and the weight updating schemes
would not be efficient.
B. RLL and Transition Matrix
A family of Biquality Learning algorithm has been pio-
neered by Patrini with [89] from the Robust Learning to Label
Noise literature. These algorithms try to estimate the per class
probabilities of label flip into another class (of the Kclasses)
which defines the Transition Matrix T.
(i, j)K2, T(i,j )=P(YU=j|YT=i)(3)
Patrini proposed in [89] to used the Transition Matrix T
to adapt any supervised loss functions Lto learning with
label noise. The two corrections proposed are : (i) the forward
loss correction: L(f(X), Y ) = L(T>·f(X), Y )and (ii) the
backward loss correction: L(f(X), Y ) = T1·L(f(X), Y ).
When no trusted samples are available as in [89], Patrini
proposed to use anchor points in order to estimate T. An
anchor point from the i-th class is the point with the highest
probability to be from the i-th class from a given dataset.
iK, Ai= argmax
Thanks to this definition Patrini propose an estimator of the
Transition Matrix :
Finally the procedure to learn a model fthat minimizes L
on untrusted data with Patrini’s approach is in two steps. First
learn fmodel on untrusted data with a loss L. Estimate the
Transition Matrix thanks to ˆ
Twith Equation 5. Then learn a
model fwith either Lor L.
This algorithm, designed for Robust Learning to Label
noise can easily be adapted to Biquality Learning. Hendrycks
proposed one adaptation in [14] with some changes to Patrini’s
approach. As trusted data are available, there is no more the
need to use anchor points to represent our trusted concept.
So another estimator for the Transition Matrix is proposed
by learning a model fUon untrusted data, and making
probabilistic predictions with fUon the trusted dataset DT
and comparing it to the trusted labels yT:
T||fU(zi)|| (6)
where Di
T={∀(x, y)DT|y=i}. Then for the final step,
Hendrycks proposed to learn fwith the corrected forward loss
Lon the untrusted data, and the uncorrected loss Lon the
trusted data. Thus GLC is an example of a Biquality Learning
algorithm that has been demonstrated to be quite efficient on
At Random supervision deficiencies.
C. Covariate Shift
Covariate Shift literature has also inspired people to adapt
these algorithms to Biquality Learning. The algorithm with the
most influence in this regard is called Importance Reweighting
[90], which aims was to give high weights to source samples
that were similar to the target samples, and low weights
when they were not similar. This objective fits well with
Not At Random (or sample dependent) corruptions as the
correction made to the untrusted dataset is per sample with
this algorithm family. Multiple approaches has been inspired
by this literature.
The key idea of this algorithm family is to define a loss
function ˜
Lsuch that learning a model fon DUthat minimizes
Lis equivalent to using the original loss function Lon DT
in the risk estimate. The following equations show how ˜
appears from the risk estimate R:
R(X,Y )T,L (f) = E(X,Y )T[L(f(X), Y )]
=E(X,Y )U[PT(X, Y )
PU(X, Y )L(f(X), Y )]
=E(X,Y )U[βL(f(X), Y )]
=R(X,Y )U, ˜
However this newly defined loss function ˜
Lcan be hard to
estimate and thus approaches have been proposed to further
simplify the weight estimation.
For example, Importance Reweighting for Biquality Learn-
ing (IRBL) [24] uses the biquality hypothesis that the distri-
bution P(X)is the same in the trusted and untrusted datasets.
By using Bayes Formula we have a new expression for β:
βIRBL =PT(X, Y )
PU(X, Y )=PT(Y|X)P(X)
First, the vector of ratios between PT(Y|X)and PU(Y|X)
is estimated by the term fT(xi)fU(xi), using the models
fTand fUlearn on DTand DU. For each untrusted example,
the weight ˆ
βIRBL is the yi-th element of this vector; while
βIRBL is fixed to 1 for the trusted examples. Then, the final
classifier is learned from DTDUby minimizing ˜
Another algorithm has been proposed in [91] named Dy-
namic Importance Reweighting (DIW) by writing Equation 8
in a more traditional way with Bayes Formula.
βDIW =PT(X, Y )
PU(X, Y )=PT(X|Y)PT(Y)
To estimate βDIW, the trick is to select both sub-samples of
DTand DUwith samples of the same classes and then use an
Density Ratio Estimator [92] such as Kernel Mean Matching
(KMM) [93], [94]. Then a final classifier is learned on DUby
minimizing ˜
L. One particular issue of this algorithm is that
KMM is learned by optimizing a quadratic program, K-times
per batch, that leads to high algorithm complexity especially
in the case of massive multiclass classification.
IRBL and DIW are two new Biquality Learning algorithms
that work on NAR cases.
In this paper, we propose a unified view of Weak Supervised
Learning to cope with the shortcomings of the supervision in
the field of Machine Learning. We discussed these shortcom-
ings through a cube along with three axes corresponding to
the characteristics of training labels (inaccurate, inexact and
incomplete). The detailed presentation of these axes gives an
insight the different existing learning approaches which can
be more subtly position on the cube. In this way, the links
between some subfields of WSL with Biquality Learning are
highlighted, showing how the algorithms of the latter field can
be used within the framework of WSL.
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