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Contrastive Representations for Label Noise Require Fine-Tuning

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This paper has been accepted at the IAL@ECML Workshop 2021 ( -------- "In this paper we show that the combination of a Contrastive representation with a label noise-robust classification head requires fine-tuning the representation in order to achieve state-of-the-art performances. Since fine-tuned representations are shown to outperform frozen ones, one can conclude that noise-robust classification heads are indeed able to promote meaningful representations if provided with a suitable starting point. Experiments are conducted to draw a comprehensive picture of performances by featuring six methods and nine noise instances of three different kinds (none, symmetric, and asymmetric). In presence of noise the experiments show that fine tuning of Contrastive representation allows the six methods to achieve better results than end-to-end learning and represent a new reference compare to the recent state of art. Results are also remarkable stable versus the noise level."
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Contrastive Representations
for Label Noise Require Fine-Tuning
Pierre Nodet1,2, Vincent Lemaire1,
Alexis Bondu1, and Antoine Cornu´ejols2
1Orange Labs, Paris & Lannion, France
2AgroParisTech, Paris, France
Abstract. In this paper we show that the combination of a Contrastive
representation with a label noise-robust classification head requires fine-
tuning the representation in order to achieve state-of-the-art perfor-
mances. Since fine-tuned representations are shown to outperform frozen
ones, one can conclude that noise-robust classification heads are indeed
able to promote meaningful representations if provided with a suitable
starting point. Experiments are conducted to draw a comprehensive pic-
ture of performances by featuring six methods and nine noise instances
of three different kinds (none, symmetric, and asymmetric). In presence
of noise the experiments show that fine tuning of Contrastive represen-
tation allows the six methods to achieve better results than end-to-end
learning and represent a new reference compare to the recent state of
art. Results are also remarkable stable versus the noise level.
1 Introduction
Deep Learning (DL) paradigm has proved very powerful in many tasks, however
recent papers [34, 49] have shown that “noisy labels” are a real challenge for
end-to-end deep learning architectures. Their test performance is found to dete-
riorate significantly even if they are able to learn perfectly the train examples.
This problem has attracted a lot of suggestion in many recent papers.
Zhang et al. [50] conducted experiments to analyze the impact of label noise
on deep architectures, and they found that the performance degradation mainly
comes from the representation learning rather than the classification part. It
therefore appears very difficult to learn a relevant representation in the presence
of label noise, in an end-to-end manner.
To tackle this problem, one option is to exploit an already existing repre-
sentation which has been learned in an unsupervised way. In particular, Self
Supervised Learning [24] (SSL) gathers an ensemble of algorithms which auto-
matically generate supervised tasks from unlabeled data, and, therefore to learn
representations from examples that are not affected by label noise. An example
of SSL algorithm is Contrastive Learning [21], where a representation of the data
is learned by making feature vectors from similar pictures (i.e. generated from
the same original picture by using two different transformer functions) to be
close in the feature space whereas feature vectors from dissimilar pictures are to
arXiv:2108.09154v1 [cs.LG] 20 Aug 2021
2 P. Nodet et al.
be far apart. In [13], the authors propose to initialize the representation with a
pre-trained Contrastive Learning one, and then, to use the noisy labels to learn
the classification part and fine-tune the representation. It appears that this ap-
proach clearly outperforms the end-to-end architecture, where the representation
is learned from noisy labels.
But questions remain: is this performance improvement only attributable to
the quality of the Contrastive Representation used (i.e. the starting point of
fine-tuning)? Or is the fine-tuning step able to promote a better representation?
To answer these questions this paper examines the different possibilities to learn
a DL architecture in presence of label noise: (i) end-to-end learning (ii) learning
only the head part when freezing a contrastive representation and (iii) fine tuning
the later representation.
The rest of this paper is organized as follows. The section 2 provides a brief
overview of the main families of algorithms dedicated to fight the label noise
underlying the issue of preserving a good representation in spite of label noise.
Section 3 then describes the experimental protocol. The section 4 will present
the results and a deep analysis which will allow us to answer the questions above.
The last section raises an interesting conclusion and provides some perspectives
for future work.
2 Representation Preserving with Noisy Labels
This section presents a brief overview of the state of the art on learning deep
architecture with noisy labels emphasizing how these methods preserve, to some
extent, the learned representation in the presence of label noise. For an extended
overview, the reader may look [43].
2.1 Preserving by Recovering
The dominant approach to preserve the learned representation is to recover a
clean distribution of the data from the noisy dataset. It mostly consists in finding
a mapping function from the noisy to the clean distribution thanks to heuristics,
algorithms or machine learning models. Three different ways of recovering the
clean distribution are usually put forward [36]: (i) sample reweighting; (ii) label
correction and (iii) instance moving.
Recovering by Reweighting - The sample reweighting methodology aims at
assigning a weight to every samples such that the reweighted population behaves
as being sampled from the clean distribution. The Radon-Nikodym derivative
(RND) [35] of the clean concept with respect to the noisy concept is the function
that defines the perfect reweighting scheme. Many algorithms therefore rely on
providing a good estimation of the RND by learning it from the data using Meta
Learning [38] or minimizing the Maximum Mean Discrepancy of both distribu-
tions in a Reproducing kernel Hilbert space [8, 32]. Many of these methods are
inspired by the covariate shift problem [14, 20]. Other algorithms rely on dif-
ferent reweighting schemes that do not involve the RND as done, for instance,
Contrastive Representations for Label Noise Require Fine-Tuning 3
in Curriculum Learning [2]. They are described in details later in this section.
By doing sample reweighting, algorithms evaluate whether or not a sample is
deemed to have been corrupted and assign a lower weight to a suspect sample
so that its influence on the training procedure is lowered. The hope is that clean
samples are sufficient to learn high-quality representations
Recovering by Relabelling - Another way to recover the clean distribution
from the noisy data is to correct the noisy labels. One great advantage over
sample reweighting is that corrected samples can be fully used during the training
procedure. Indeed, when a sample is corrected, it will count as one entire sample
in the training procedure (gradient descent for example), whereas a reweighted
noisy sample would get a low weight and would not be used significantly in the
training procedure. Thus, when done effectively, label correcting might get better
performance. Meta Label Correction (MLC) [42] is an example of this approach
where the label correction is done thanks to a model learned using meta learning.
One downside of label correction, however, is that the label of a clean sample
can get “corrected” or the label of a noisy sample can get changed to a wrong
label. Label correcting algorithm assign the same weight to all training examples,
even though they might have “corrected” a label based on shaky assumptions.
By contrast, Sample reweighting will assign a low weight if the algorithm is not
confident in whether the sample is clean or noisy.
Recovering by Modifying - A third way to recover the clean distribution is
by modifying the sample itself so that its position in the feature space gets closer
or is moved within an area for its label that seems more appropriate (i.e. obeying
regularisation criteria). Finding a transformation in the latent space itself has
the advantage to require less labelled samples, or even none at all, as the work
is performed on distance between samples themselves, like for example in [40].
2.2 Preserving by Collaboration
Multiple algorithms and agreements measures have been used in many sub-fields
of machine learning such as ensembling [4,10,11] or semi supervised learning [3,
48]. They can be adapted to learn with noisy labels by relying on a disagreement
method between models in order to detect noisy samples. When the learned
models disagree on predictions for the label of a sample, this is considered as
a sign that the label of this sample may be noisy. When the models used are
diverse enough, these methods are often found to be quite efficient [17,46].
However these algorithms suffer from learning their own biases and diversity
needs to be introduced in the learning procedure. Using algorithms from different
classes of models and different origins can increase the diversity among them by
introducing more source of biases [28]. Alternating between learning from the
data and from the other models is another way to combat the reinforcement of
the models’ biases [46]. These algorithms rely on carefully made heuristics to be
4 P. Nodet et al.
2.3 Preserving by Correcting
When learning loss base models, such as neural networks, on label noise, the
loss value of a training example can be a discriminative feature to decide if
its label is noisy. Deep neural networks seem to have the property that they
first learn general and high level patterns from the data before falling prey
to overfitting the training samples, especially in the presence of noisy labels
[1, 30]. As they are “learned” at a later stage, these noisy examples are often
associated with a high loss value [16] which may then highly influences the
training procedure and perturb the learned representation [49]. A way to combat
label noise is accordingly to focus first on small loss and easy examples and
keep the high loss and hard examples for the end of the training procedure.
Curriculum Learning [2] is a way to employ this training schema with heuristic
based schemes [9,22,27,31] or schemes learned from data [23,41]. This class of
algorithm has the same properties as the ones relying on importance reweighting,
but maybe more adapted to training with iterative loss based algorithms such
as neural networks or linear models.
Instead of filtering or reweighting samples based on their loss values, one
could try to correct the loss for these samples using the underlying noise pat-
tern. Numerous method have been doing so by estimating the noise transition
matrix for Completely at Random (i.e uniform) and At Random (i.e class de-
pendent) noise [19, 37, 42]. This category of algorithms are still to be tested on
more complex noises scenarios such as Not at Random (i.e instance and class
dependent) noise.
2.4 Preserving by Robustness
The last identified way to preserve the learned representation of a deep neural
network in presence of label noise is by using a robust or regularized training
procedure. This can take multiple forms from losses to architectures or even
optimizers. One of them are Symmetric Losses [5,12,39]. A symmetric loss has the
property that: x X ,Py∈Y L(f(x), y) = cwhere cR. These losses have been
proven to be theoretically insensitive to Completely at Random (CAR) label
noise. Recently, modified versions of the well-known Categorical Cross Entropy
(CCE) loss have been designed in order to be more robust and thus more resistant
to CAR label noise as is the case for the Symmetric Cross Entropy (SCE) loss [45]
or the Generalized Cross Entropy Loss (GCE) [51]. Both of these rely on using
the CCE loss combined with a known more robust loss such as the Mean Absolute
Error (MAE). However, the resulting algorithms often underfit in presence of too
few label noise while they are unable to learn a correct classifier with too much
label noise.
All these approaches still adopt the end-to-end learning framework, aiming
at fighting the effects of label noise by preserving the learned representation.
However they fail to do so in practice: decoupling the learning of the representa-
tion, using Self Supervised (SSL) learning, from the classification learning stage
itself and then fine tuning the representation with robust algorithms is beneficial
Contrastive Representations for Label Noise Require Fine-Tuning 5
for the model performance [13, 50]. A natural question arises about the origin of
the performance improvements, and the ability of these algorithms to learn or
promote a good representation in presence of label noise. If robust algorithms
are unable to learn a representation it should be even better to freeze the SSL
representation instead of fine tuning it.
In order to assess the origin of the improvements for different classes of
algorithms and different noise levels, we compare the above-mentioned end-to-
end approaches against each other when the representation is learned in a self-
supervised fashion by either fine tuning or freezing the representation when the
classification head is learned. Thus, any difference in the performance would be
attributable to the difference in the representation learnt.
3 Experimental Protocol
In [50], the authors showed that when using end-to-end learning, fine tuning the
representation on noisy labels harms a lot the final performance, while learn-
ing a classifier on frozen embeddings is quite robust to label noise and leads
to significant performance improvements over state-of-the-art algorithms if the
representation is learned using trustful examples. The latter can be found for in-
stance using confidence and loss value. Nonetheless it is arguable whether these
improvements were brought by an efficient self-supervised pretraining (SSL) with
SimCLR [6], a contrastive learning method, or by the classification stage of the
REED algorithm [50].
The goal of the following experimental protocol is to assess and isolate the
role of the contrastive learning stage, in the performance that can be achieved
by representative methods as presented in Section 2 about state of the art ap-
proaches. Specifically, several RLL algorithms have been chosen, one from each
of the highlighted families (see Section 2 and Table 1). For each, the difference in
performance between using contrastive learning to learn the representation and
the performance reported with the original end-to-end algorithms is measured.
These experiments seek to highlight the impact of each RLL algorithms and
assess if these are able to promote a better representation than the pretrained
contrastive representation through fine-tuning.
The rest of this section describes the experimental protocol used to conduct
this set of experiments.
3.1 The tested Algorithms
Section 2 presented an overview of the state of the art for learning with label
noise organized around families of approaches that we highlighted. Since our
experiments aim at studying the properties of each of these approaches, we
selected one representative technique from each of these families as indicated in
the following.
In the first family of techniques (recover the clean distribution), the algo-
rithms re-weight the noisy examples or attempt to correct their label. One
6 P. Nodet et al.
of these algorithm uses what is called Dynamic Importance Reweigthting
(DIW). It reweights samples using Kernel Mean Matching (KMM) [14, 20]
as is done in covariate shift with Density Ratio Estimators [44]. Because this
algorithm adapts well-grounded principles to end-to-end deep learning, it is
a particularly relevant algorithm for our experiments.
CoLearning (CoL) [46] is a good representative of the family of collaborative
learning algorithms. It uses disagreements criteria to detect noisy labels and
is tailored for end-to-end deep learning where the two models are branches
of a larger neural networks. It appears to be one of the best performing
collaborative algorithm while not resorting to complex methods such as data
augmentation or probabilistic modelling like the better known DivideMix
The third identified way to combat label noise is by mitigating the effect
of high loss samples [16] by either ditching them or using a loss correction
approach. Curriculum learning is often used to remove the examples that
are associated with high loss from the training set. (MWNet) [41] is one
the most recent approach using this technique, which learns the curriculum
from the data with meta learning. Besides, Forward Loss Correction (F-
Correction) [37] and Gold Loss Correction (GLC) [19] are two of the most
popular approaches to combat label noise by correcting the loss function.
Both seek to estimate the transition matrix between the noisy labels to the
clean labels, the first technique using a supervised approach thanks to a clean
validation set, and the second one in an unsupervised manner. Even though
many extensions of these algorithm have been developed since then [42,47], in
these experiments, we use F-Correction and GLC since they are way simpler
and almost as effective.
Lastly, in recent literature, a new emphasis is put on the research of new loss
functions that are conducive to better risk minimization in presence of noisy
labels for robustness purpose. For example, [5, 39] show theoretically and
experimentally that when the loss function satisfies a symmetry condition,
described below, this contributes to the robustness of the classifier. The
Generalized Cross Entropy (GCE) [51] is the robust loss chosen in this
benchmark as it appears to be very effective.
A note about additional requirements: These algorithms may have additional
requirements, mostly some knowledge about the noise properties. These are de-
scribed in table 1. In the experiments presented below, the clean validation
dataset is set to be 2 percent of the total training data, like in [41, 53], and the
noise probability is provided to the algorithms that need it.
A note about the choice of the pretrained architecture: We chose to use Sim-
CLR for Self-Supervised Learning (SSL) as done in [50].
SimCLR is a contrastive learning algorithm that is composed of three main
components (See Figure 1): a family of data augmentation T, an encoder network
f(·) and a projection head g(·). Data augmentation is used as a mean to generate
positive pairs of samples: a single image xis transformed into two similar images
Contrastive Representations for Label Noise Require Fine-Tuning 7
Algorithms (Date) Noise Ratio Clean Validation Family (Section)
DIW (2020) ×XReweighting (2.1)
CoLearning (2020) X×Collaborative Learning (2.2)
MWNet (2019) ×XCurriculum Learning (2.3)
F-Correction (2017) × × Loss Correction (2.3)
GLC (2018) ×XLoss Correction (2.3)
GCE (2018) × × Robust Loss (2.4)
Table 1. Taxonomy of robust deep learning algorithms studied in this paper. The
Noise Ratio column corresponds to whether the algorithm needs the noise rate (X)
to learn from noisy data or not (×). The Clean Validation column corresponds to
whether the algorithm needs an additional clean validation dataset (X) to learn from
noisy data or not (×).
xiand ˜
xjby using a data augmentation module Twith different seeds tand t0.
Then the two images go through an encoder network f(·) to extract an image
representation h, such as hi=f(˜
xi) and hj=f(˜
xj). Finally a projection head
g(.) is used to train the contrastive objective in a smaller sample space z, with
hi) and zj=g(˜
hj). The contrastive loss used is called the NT-Xent, the
normalized temperature-scaled cross entropy loss, and defined by the following
`(zi,zj) = log exp(sim(zi,zj))
k=1 exp(sim(zi,zk))(1)
where τis the temperature scaling and sim is the cosine similarity. The final
loss is computed across all positive pairs, both (i, j) and (j, i), in a mini-batch.
When the training of SimCLR is complete, the projection head g(.) is dropped
and the embeddings hare used as an image representation in downstream tasks.
Other SSL algorithms could have been used as well, such as Moco [7, 18] or
Bootstrap Your Own Latent (BYOL) [15]. However, we do not expect that the
main conclusions of the study would be much changed.
3.2 Datasets
The datasets chosen in this benchmark are two image classification datasets
namely CIFAR10, CIFAR100. They are two famous image classification datasets,
containing only clean examples and as such, we will simulate symmetric (Com-
pletly at Random) and asymmetric (At Random) noise as defined later in section
3.3. These benchmarks should be extended to other image classification datasets
such as FashionMNIST, Food-101N, Clothing1M and Webvision and to other
classification tasks such as text classification or time series classification.
8 P. Nodet et al.
Fig. 1. Figure from [6]: “A simple framework for contrastive learning of visual rep-
resentations. Two separate data augmentation operators are sampled from the same
family of augmentations (t∼ T and t0∼ T ) and applied to each data example to
obtain two correlated views. A base encoder network f(·) and a projection head g(·)
are trained to maximize agreement using a contrastive loss. After training is completed,
we throw away the projection head g(·) and use encoder f(·) and representation hfor
downstream tasks.”
3.3 Simulated Noise
As datasets chosen in Section 3.2 contains clean labels, label noise will be intro-
duced synthetically on the training samples. Two artificial noise models will be
used, a symmetric (Completely at Random) and asymmetric (At Random) noise.
Symmetric noise corrupts a label from one class to any other classes with the
same probability, meanwhile the asymmetric corrupts a label to a similar class
only. Similar classes are defined through class mappings. For CIFAR-10, the
HORSE, CAT DOG. For CIFAR-100, the class mappings are generated
from the next class in that group (where 100 classes are categorized into 20 super-
classes of 5 classes). These class mappings are the ones introduced in [37, 51].
3.4 Implementation Details
We give some implementation details for reproducibility and / or a better un-
derstanding of the freezing process in the experiments:
On CIFAR10 and CIFAR100 the SGD optimizer will be used to train the
final Multinomial Logistic Regression with an initial learning rate of 0.01,
a weight decay of 1e4and a non-Nesterov momentum of 0.9. The learning
rate will be modified during training with cosine annealing [33]. The batch
size is 128.
When doing the ”Freeze” experiments, the weights of SimCLR from [13]
will be used and will not be modified during the training procedure. All
Contrastive Representations for Label Noise Require Fine-Tuning 9
the weights up to before the projection head of SimCLR are used, then the
dimension output of the feature encoder is 2048 for CIFAR10 and CIFAR100.
The classification architecture is composed by a single linear layer with an
output dimension of 10 (or 100), corresponding to the number of classes.
Thus when trained with the Categorical Cross Entropy it corresponds to a
usual logistic regression. This classifier is going to be learned with multiple
algorithms robust to label noise. These algorithms are not modified from
their original formulation.
The ”Fine Tuning” experiments follow the same implementation as the
”Freeze” experiments. However the weights of the same pretrained SimCLR
encoder are allowed to be modified by backpropagation.
Based on their public implementation and / or article we re-implemented
all the algorithm tested (DIW [8], CoL [46], MWNet [41], F-Correction [37],
GLC [19] and GCE [51]). All these re-implemented algorithms will soon be
available as an open source library easily usable by researchers and practi-
tioners. These custom implementations have been verified to produce, under
the same condition stated in the corresponding original papers (noise mod-
els, network architectures, optimizers, ...), the same results or results in the
interval of confidence (for clean or noisy labels). We may thus be confident
that results in the different parts of the Tables 2 and 3 are comparable.
The experiments have been run multiple times for all algorithms, some
datasets, some noise models and some noise ratios with different seeds to
see the seed impact on the final performance of the classifier. For all algo-
rithms, the standard deviation of the accuracy was less than 0.1 percent.
4 Results
This section reports the results obtained using the protocol described in section
3. They are presented in the tables 2 and 3 corresponding to the two tested
datasets CIFAR10 and CIFAR100. Each table is composed of four rows subsec-
tions corresponding to the different types of representation used, which can be
learned in a End-to-End manner (A), be taken from an already existing SSL
model, either Frozen (B) or Fine tuned (C). Moreover they are composed of two
columns subsections corresponding to the noise model used to corrupt samples
(symmetric or asymmetric).
These tables present the results from different studies: (A) The first part
of these tables about “End-to-End learning” are results reported in the respec-
tive papers [8, 19, 37, 41, 46, 51] or reported in [13]; (B) The second part about
“Freeze” experiments conducted in this paper, are made by re-implementing the
referred algorithms from scratch; (C) The “Fine Tuning” experiments are results
reported in [13].
The interpretation of the Table 2 and 3 will be done in two times, first
a comparison between whole blocks (as (A) against (B)) will give insights on
how deep neural networks learn representations on noisy data and how robust
algorithms helps to improve the learning process or helps to preserve a given
10 P. Nodet et al.
Clean Symmetric Asymmetric
0 20 40 60 80 90 95 20 40
DIW [8]
End-to-End (A)
80.4 76.3 84.4
CoL [46] 93.3 91.2 49.2 88.2 82.9
MWNet [41] 95.6 92.4 89.3 84.1 69.6 25.8 18.5 93.1 89.7
F-Correction [37] 90.5 87.9 63.3 42.9 90.1
GLC [19] 95.0 95.0 95.0 95.0 90.0 80.0 76.0
GCE [51] 93.3 89.8 87.1 82.5 64.1 89.3 76.7
Freeze (B)
91.3 91.2 90.8 90.5 89.8 89.2 88.1 91.0 90.6
CoL 91.1 91.1 90.9 90.6 89.9 89.4 88.8 90.8 89.9
MWNet 91.3 91.2 90.8 90.6 89.8 88.2 82.4 90.9 86.4
F-Correction 90.8 90.5 90.1 89.6 88.4 88.0 88.1 88.9 88.4
GLC 90.7 89.7 90.0 89.5 89.0 88.5 88.3 88.7 88.2
GCE 91.1 90.8 90.7 90.5 90.4 90.0 89.1 90.9 89.0
Fine Tuning (C)
94.5 94.5 94.5 94.5 94.0 92.0 89.1 94.2 93.6
CoL 93.9 94.6 94.6 94.2 93.6 92.7 91.7 94.0 93.7
MWNet [13] 94.6 93.9 92.9 91.5 90.2 87.2 93.7 92.6
F-Correction 94.0 93.4 93.1 92.9 92.3 91.4 90.0 93.6 92.8
GLC 93.5 93.4 93.5 93.1 92.0 91.2 88.3 93.2 92.1
GCE [13] 94.6 94.0 92.9 90.8 88.4 83.8 93.5 90.3
Table 2. Final accuracy for the different models on CIFAR10 under symmetric and
asymmetric noises and multiple noise rates.
representation. Then in a second time comparisons in a given block will be made
against multiple algorithms to see how well these conclusions works on different
preservation families given in Section 2.
First, we observe when comparing section (A) and (B) from both tables
that ”Freeze” experiments consistently outperforms ”End-to-End” experiments
as soon as the data stop being perfectly clean. Using a pretrained self-supervised
representation such as SimCLR improves significantly the performances of the
final classifier. Outside of well controlled and perfectly clean datasets all selected
algorithms are not able to learn a good enough representation from the noisy
data and are beaten by a representation learned without resorting to using given
labels. Robust Learning to Label noise algorithms, especially designed for deep
learning, can preserve an already good representation from noisy labels but are
unable to learn a good representation from scratch.
Then, we observe when comparing section (B) and (C) from both tables that
”Fine Tuning” experiments consistently outperforms ”Freeze” at noise rates
less than 80 for the symmetric case and less than 40 for the asymmetric case.
The nature of the final classifier used after the learned representation partially
explains these results; we used a single dense layer (see Section 3.4). This classifier
may under-fit as the number of learnable parameters might be too low to actually
fit complex datasets such as CIFAR10 and CIFAR100 even with a good given
representation. Using more complex classifiers such as Multi-Layer Perceptron
could have led to comparable performances than fine tuning even for low noise
rates. This point leaves room for further investigation. Having the possibility to
Contrastive Representations for Label Noise Require Fine-Tuning 11
Clean Symmetric Asymmetric
0 20 40 60 80 90 95 20 40
DIW [8]
End-to-End (A)
53.7 49.1 54.0
CoL [46] 75.8 73.0 32.8
MWNet [41] 79.9 74.0 67.7 58.7 30.5 5.2 3.0 71.5 56.0
F-Correction [37] 68.1 58.6 19.9 10.2 64.2
GLC [19] 75.0 75.0 75.0 62.0 44.0 24.0 12.0 75.0 75.0
GCE [51] 76.8 66.8 61.8 53.2 29.2 66.6 47.2
Freeze (B)
65.6 65.1 64.0 62.9 59.0 53.3 42.5 61.7 49.0
CoL 65.8 65.0 64.0 63.4 62.3 60.0 57.0 64.1 58.6
MWNet 66.6 66.6 66.2 65.4 63.7 59.8 49.5 64.8 54.5
F-Correction 66.5 64.7 61.8 58.8 54.5 51.7 50.8 58.4 56.5
GLC 58.5 57.8 52.3 51.1 41.6 40.1 35.3 51.4 50.3
GCE 63.5 62.9 61.5 60.0 55.7 51.0 49.9 51.2 48.3
Fine Tuning (C)
73.8 74.9 74.9 74.5 70.2 62.3 50.4 71.8 62.8
CoL 73.7 74.8 74.8 75.0 73.2 67.3 62.0 72.6 70.3
MWNet [13] 75.4 73.2 69.9 64.0 57.6 44.9 72.2 64.9
F-Correction 69.8 70.1 69.1 69.5 66.9 62.1 57.0 70.3 66.2
GLC 69.7 69.4 68.6 62.5 50.4 32.1 18.7 68.2 62.3
GCE [13] 75.4 73.3 70.1 63.3 55.9 45.7 71.3 59.3
Table 3. Final accuracy for the different models on CIFAR100 under symmetric and
asymmetric noises and multiple noise rates.
fine tune the representation to better fit the classification task induces the risk
to actually degrade it.
Outside of well controlled and perfectly clean datasets, practitioners should
first consider to learn a self-supervised representation and then either fine tune
it or freeze it with classifier learned with robust algorithms. Self-Supervised
Learning (SSL) algorithm such as SimCLR seems to perfectly fit this task, but
other SSL algorithms could be used and explored.
Another observation from this benchmark is about the difference in perfor-
mance between all the tested algorithms. Indeed, if we consider part (B) of Table
2, for both noise models and all noise rates, the performances between the al-
gorithms are close, around 0.1 point in accuracy with some exceptional data
points. It shows that even complex algorithms have a hard time beating simpler
approaches when they are compared with an already learned representation.
The same observation can be done for the part (B) of Table 3 (for CIFAR
100), especially for the symmetric noise. However the differences between al-
gorithms are better put in perspective with this more complex dataset which
contains 10 time more classes and 10 time less samples per classes. We notice
that some algorithms start to struggle at high symmetric noise rate or for the
more complex asymmetric noise model. For example, GLC is under-performing
against competitors for all cases and is under-performing against its end-to-end
version. One reason could be the small size used for the validation dataset as the
transition matrix is evaluated on it in a supervised manner. The small number of
samples may impact the performance of the transition matrix estimator. Much
less so than the estimator proposed by F-Correction which seems to perform fine
12 P. Nodet et al.
even on CIFAR100 for all symmetric noises, yet only above average on asym-
metric noises. Seeing F-Correction and GLC not performing well on asymmetric
noise for both dataset is surprising as these algorithms were both particularly
designed for this case.
Lastly we observe on both Tables 2 and 3 that algorithms with additional
knowledge on the noise model (see Table 1) have an edge over algorithms that
do not, especially on the hardest cases with more classes, higher noise ratio or
more complex noise model. CoL requires the noise ratio as its efficiency relies
on the hyper parameters value corresponding to the injection of pseudo labels
and confidence in model prediction that are dependent of the noise ratio. CoL
emerges among the most well rounded and most efficient algorithm for all noise
models, noise rates and datasets thanks partially to this additional knowledge.
On the other hand, GLC, DIW and MWNet require an additional clean valida-
tion dataset in order to estimate the noise model or a proxy of it to correct the
learning procedure on the noisy dataset. We could expect these algorithms to
perform better than CoL as they would be able to deal with more complex noise
models and have a fine-grained policy for correcting noisy samples. Still these
algorithms are not able in these experiments to get a better accuracy than CoL
and perform on par with it.
Finally we need to emphasize that only two datasets have been used in this
study, specially two datasets about image classification. In order to stronger our
claims, more experiments should be conducted.
5 Conclusion
In this paper our contribution was to suggest new insights about decoupling
against end-to-end deep learning architectures to learn, preserve or promote a
good representation in case of label noise. We presented (i) a new view on a
part of the state of the art: the ways to preserve the representation (ii) and an
empirical study which completes the results and the conclusions of other recent
papers [13, 50,52]. Experiments conducted draw a comprehensive picture of per-
formances by featuring six methods and nine noise instances of three different
kinds (none, symmetric, and asymmetric). Our added value for the empirical
study is the comparison between the ”freeze” and the ”fine tuning” results.
One conclusion we are able to draw is that designing algorithms that preserve
or promote good representation under label noise is not the same as designing
algorithms capable of learning from scratch a good representation under label
noise. To make end-to-end learning succeed in this setup researchers should take
a better approach when designing such algorithms.
Another element that emerged from the experiments was the efficiency of
both freeze and fine tuning approaches in comparison to the end-to-end learning
approach. Even the most complex algorithms such as DIW when trained in an
end-to-end manner are not able to beat simple robust loss as GCE when trained
with fine tuning. It questions usual experimental protocols of Robust Learning
to Label (RLL) noise papers and questions the recent advances in the field.
Contrastive Representations for Label Noise Require Fine-Tuning 13
Evaluating RLL algorithms with pretrained architectures should be the norm as
it is easy to do so and the most efficient way for practitioners to train model on
noisy data.
One more strong point in this conclusion is that in presence of noise the
experiments show that fine tuning of Contrastive representation allows the six
methods to achieve better results than their end-to-end learning version and
represent a new reference compare to the recent state of art. Results are also
remarkable stable versus the noise level.
Since fine-tuned representations are shown to outperform frozen ones, one can
conclude that noise-robust classification heads are indeed able to promote mean-
ingful representations if provided with a suitable starting point (contrastingly to
readers of [13, 52] who might prematurely jump to the inverse conclusion).
However these experiments could be extended to be more exhaustive in two
ways: (i) SimCLR is not the only recent and efficient contrastive learning algo-
rithms, MOCO [7, 18] or Bootstrap Your Own Latent (BYOL) [15] could have
been used as said earlier in the paper, but other self-supervised or unsupervised
algorithms could have been used such as Auto-Encoder [26] or Flow [25]; (ii)
experiments could be extended to datasets from other domains such as text
classification or time series classification.
We would like to thank the anonymous reviewers for their careful, valuable and
constructive reviews as well as the words of encouragement on our manuscript.
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