Fashioning with Networks: Neural Style Transfer to Design
University Of Maryland
Baltimore County (UMBC),
University Of Maryland
Baltimore County (UMBC),
University Of Maryland
Baltimore County (UMBC),
Convolutional Neural Networks have been highly successful
in performing a host of computer vision tasks such as object
recognition, object detection, image segmentation and tex-
ture synthesis. In 2015, Gatys et. al  show how the style
of a painter can be extracted from an image of the painting
and applied to another normal photograph, thus recreating
the photo in the style of the painter. The method has been
successfully applied to a wide range of images and has since
spawned multiple applications and mobile apps. In this pa-
per, the neural style transfer algorithm is applied to fashion
so as to synthesize new custom clothes. We construct an
approach to personalize and generate new custom clothes
based on a user’s preference and by learning the user’s fash-
ion choices from a limited set of clothes from their closet.
The approach is evaluated by analyzing the generated im-
ages of clothes and how well they align with the user’s fash-
•Computing methodologies →Computer vision; Ma-
chine learning approaches; Neural networks;
Convolutional Neural Networks, Personalization, Fashion,
Neural Networks, Style Transfer, Texture Synthesis
There have been recently impressive advances in computer
vision tasks like object recognition and detection, segmen-
tation . The revolution started with Krizhevsky
et. al  substantially improving object recognition on
the Imagenet challenge using convolutional neural networks
(CNN). This led to research and subsequent improvements
in many tasks related to fashion such as classiﬁcation of
clothes, predicting diﬀerent kinds of attributes of a spe-
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ciﬁc piece of clothing, and improving the retrieval of images
. Giants of e-commerce are expanding their
investment in fashion. Recently, Amazon patented a system
to manufacture clothes on demand . Also, they have
started shipping their virtual assistant Echo with an inte-
grated camera that clicks a picture of the user’s outﬁt and
rates its style . StitchFix1aims to simplify the user’s expe-
rience to shop online. As the online fashion industry looks to
improve the kind of clothes that are recommended to users,
understanding the personal style preferences of users and
recommending custom designs becomes an important task.
Personalization and recommendation models are a well
researched area that include methods from collaborative ﬁl-
tering  to content-based recommendation systems (e.g.,
probabilistic graph models, neural networks) as well as hy-
brid systems that combine both. Collaborative ﬁltering 
tries to analyze user behaviour and preferences and align
users to predeﬁned patterns so as to recommend a product.
Content-based methods recommend a product based on its
attributes or features that the user is searching for. A hy-
brid system (knowledge-based system ) incorporates user
preferences and product features to recommend an item to
While the above techniques retrieve the product (or its
image) we seek to synthesize new personalized merchandise.
Texture synthesis tries to learn the underlying texture of
an image in order to generate new samples with the same
texture. The research in this space  is largely focused on
parametric and non-parametric methods. Non-parametric
methods try to resample speciﬁc pixels from the image or
adopt speciﬁc patches from the original to generate the new
image . Parametric methods deﬁne a statisti-
cal model that represents the texture . In 2015,
Gatys et. al.  designed a new parametric model for tex-
ture synthesis based on convolutional neural networks. They
model the style of an image by extracting the feature maps
generated when the image is fed through a pre-trained CNN,
in this instance using a 19 layer VGGNet. They successfully
separate the style and content of an arbitrary image and
demonstrate how the other image can be stylized using the
textures of the prior.
Although Convolutional Neural Networks provide state-
of-the-art performance for multiple computer vision tasks,
their complexity and opacity has been a substantial research
question. Visualizing the features learned by the network
has been addressed in multiple eﬀorts. Zeilar et. al  use
arXiv:1707.09899v1 [cs.CV] 31 Jul 2017
Figure 1: (a) and (b) provide the shape & style respectively (c) Final Design
a deconvolution network to reconstruct the features learned
in each layer of the CNN. Simoyan et. al.  backpropagate
the gradients generated for a class with respect to the input
image to create an artiﬁcial image (the initial image is just
random noise) that represents the class in the network. The
separation of style and content in an image by Gatys et. al.
 shows the variant (content) and invariant (style) parts
of the image.
Our contribution in this paper is a pipeline to learn the
user’s unique fashion sense and generate new design pat-
terns based on their preferences. Figure 1 shows a sample
clothing item generated using neural style transfer. The ﬁrst
clothing item given by the user provides the shape for the
new dress. The second is initially provided by the user from
his/her closet to learn their preference. The third is the ﬁnal
generated design for the user (the generated sample contains
styles from multiple pieces of the user’s clothing).
The following sections discuss the related work, how neu-
ral style transfer works, our system architecture, experi-
ments conducted and their results.
2. RELATED WORK
Prior research on fashion data in the computer vision com-
munity has dealt with a whole range of challenges including
clothes classiﬁcation, predicting attributes of clothes and the
retrieval of items . Liu et. al  create a ro-
bust fashion dataset of about 800,000 images that contains
annotations for various types of clothes, their attributes and
the location of landmarks as well as cross-domains pairs.
They also design a CNN to predict attibutes and landmarks.
The architecture is based on a 16 layer VGGNet and adds
convolution and fully-connected layers to train a network to
predict them. Phillip et. al  perform image to image
translation using a conditional adversarial network. They
perform experiments to generate various fashion accessories
when provided with a sketch of the item.
We use a 19-layer pre-trained VGGNet  that is trained
on the imagenet dataset . The network consists of 8 con-
volutional layers and 3 fully-connected layers. It is trained
to predict 1000 classes (from the Imagenet challenge). The
network is known to be robust and the features generated
have been used to solve multiple downstream tasks. Gatys et
al. use the pre-trained VGGNet to extract style and content
Johnson et. al.  create an image transformation net-
work trained to transform the image with the given style. A
feed-forward transformation network is trained to run real-
time using perceptual loss functions that depend on high-
level features from a pre-trained loss network rather than
the per-pixel loss function based on low level pixel informa-
tion. The trained network does not start transforming the
image from white-noise but generates the output directly,
thus speeding up the process.
Gatys et al. [7, 8] describes the process of using image
representations encoded by multiple layers of VGGNet to
separate the content and style of images and recombine them
to form new images. The idea of style extraction is based
on the texture synthesis process that represents the texture
as a Gram Matrix of the feature maps generated from each
convolutional layer. The style is extracted as a weighted set
of gram matrices across all convolutional layers of the pre-
trained VGGNet when it processes an image. The content
is obtained from feature maps extracted from the higher
layers of the network when the image is processed. The style
and content losses are computed as the mean squared error
(MSE) between the features maps and Gram matrices of the
original image and a randomly generated image (initiated
from white noise). Minimizing the loss transforms the white
noise to a new artistic image.
We use the method described above to generate new fash-
This section describes how the style and content is ex-
tracted from an image using neural style transfer . We
use the implementation given by , a pre-trained 19 layer
VGGnet model (VGG-19) that takes a content image and a
set of style images as input.
Consider an input image xand convolutional neural net-
work NN . Every convolution layer lin the convolutional
network has Nldistinct ﬁlters. Upon completion of the con-
volution operation (and the activation function being ap-
plied), let the feature map computed have height hand
width w. The ﬂattened map (into a single vector) has a
size of Ml= 1 ×(hxw). Thus, the feature maps at every
layer lcan be given as Fl
ij ∈ RNl×Mlwhere Fl
the activation of the ith ﬁlter at position k.
3.1 Style Extraction
Figure 2: Overall System Architecture. A1...Anare all the attributes in the dataset , A1...Akare set of
attributes given by the user. Lis the total loss between gram matrix of modiﬁed (iteratively) UCO image &
gram matrices from user’s personal style store (for A1...Ak). In the ﬁrst phase the user provides the system
access to his / her closet images from where the user’s fashion preferences are learned. In phase two, the
user gives his / her choices (attributes such as Striped Top or Chiﬀon ) with the desired outline of piece of
clothing to get the new custom design.
The Gram matrix at layer lis given by Gl∈RNl×Nlwhere
ij is calculated by the dot product of the feature maps i
and jfor layer l:
The dot product computes the similarities between fea-
ture maps. Thus the Gram matrix Glinvariably contains
image points that are consistent between the maps while
inconsistent features become 0.
Consider two images x(input image used to transfer the
style) and ˆx(a randomly generated image from white noise).
Let their corresponding Gram matrices be Gland ˆ
style loss function is then computed for every layer as the
mean squared error (MSE) between Gland ˆ
Elis the style loss.
3.2 Content Extraction
The feature maps from the higher layers in the model give
a representation of the image that is more biased towards
the content . We use the feature representations of the
conv 4 2 layer to extract content. Given the feature repre-
sentations in layer lof the original image xand the generated
white noise image ˆxas Fland ˆ
Flrespectively, we deﬁne the
content loss as the mean squared diﬀerence between the two:
Lcontent(x, ˆx, l) = 1
The derivative of this loss with respect to the feature map
at layer lgives the gradient used to minimize the loss:
Fl)ij ,if Fl
4. SYSTEM ARCHITECTURE
Figure 2 shows the entire pipeline to personalize and de-
sign custom clothes for the user. There are four modules to
the architecture, namely, preprocessing, personal style store
creation, style transfer and post-processing to generate the
ﬁnal design. The following section discusses these modules
in more detail.
To minimize the complexity of the problem, we consider
images from the DeepFashion dataset  that have a white
background. The images contain only clothing objects with
no humans or other artifacts. They are only upper-body or
full-body apparel pieces.
All images are resized to 512 x 512. The image is resized
not by expanding/contracting the image, but by creating a
temporary white background image of the above mentioned
Figure 3: Evaluation model for predicting attribute labels on separate training and test generation images
size. The original image is then placed at the center of that
temporary image. This resizes the image to the expected
size without deforming it. Also, the mask of the image is
extracted and stored using the grabcut utility . This
mask is used in the postprocessing step to get rid of patterns
lying beyond the contours of the apparel. The attributes for
the clothes are assumed to be provided and automatically
labeling them is beyond the scope of this paper.
4.2 Creating a Personal Style Store
To learn the user’s fashion preferences, the user initially
provides the set of clothes from his / her closet. The Gram
matrices Gl(eq.1) of all the clothes with their annotated
attributes are calculated. Tensorﬂow  allows us to get the
partially computed functions Elin 2 (where the gram matri-
ces for Glare computed ﬁrst and then ˆ
Gllater). The style
losses Elare thus stored in a dictionary with the associated
attributes. A personal style store is constructed for each
4.3 Style Transfer
To perform style transfer, two inputs are necessary. As
shown in ﬁgure 2, the user inputs a list of attributes that
he/she will like in their new garment. This list can be at-
tributes like print and stripes or fabric such as chiﬀon. In
the current system, style is learned only for attribute types
texture and fabric. The dress shape is not considered as a
representation of the style of that object. Apart from these
attributes, the user also gives an image that contains the
shape of the dress they desire. This is called the User Cho-
sen Outline (UCO). Let the attributes of the dresses in the
closet be A1...An. The selected user attributes are A1...Ak
where k << n. The set of style loss functions having the cor-
responding attributes are selected from the user’s personal
style store. Although the style’s extracted from the user’s
closet as a whole represent the his/her fashion sense, we
pick the style functions of the chosen attributes because we
assume the user’s mental model of dress is likely to be sim-
ilar to the styles extracted for those attributes. All selected
functions are then combined to get a singular representation
of the user’s fashion choices.
For a style image xand the initialized image ˆx, the style
loss can be given as,
Ls(ˆx, x) =
where Lsis the style loss for a single image.
The combined loss is given by:
Here, Lstyle is the style loss computed over Sselect func-
The number of images for every attribute picked depends
on the distribution of the particular attribute across the en-
tire list of images present. The higher the frequency of the
attribute in the distribution, the higher is the bias towards
a certain label and suppresses the eﬀect of the others. This
makes certain image characteristics more pronounced in the
ﬁnal dress than others. Hence, to oﬀset the bias the weight
Total Loss is the summation of the style and content
Ltotal =αLcontent(C, x) + βLsty le(S, x) (7)
Here, αand βare the weights assigned to the content and
style losses respectively. C is the user chosen outline (UCO).
An LBFGS optimizer is used to minimize the loss. The
output image is then post-processed to get the ﬁnal image.
The objective is to minimize the content and style losses.
The output image contains patches of patterns transferred
across the entire image. We resize the image to its orig-
inal dimensions and apply the mask (of the UCO image)
Figure 4: Multiple styles reinforced in a content image
extracted to white out the background and get the trans-
formed clothing object as the ﬁnal resultant dress.
5. EXPERIMENTS & RESULTS
We present two approaches to evaluate the results of per-
sonalization using style transfer.
5.1 Predicting Attribute labels
Quantitative evaluation for personalization models is a
challenging task. A standard approach is to create a sur-
vey of mechanical turk and ask users if the styles have been
transferred properly and if the new dress designs are per-
sonalized given a wardrobe. But fashion presents a unique
challenge as it is highly dependent on the user’s taste for dif-
ferent kinds of clothing. Instead a diﬀerent tact is applied.
Figure 3 shows how the evaluation is performed. We check
if style is imparted on the given UCO image by verifying if
the classiﬁer is able to identify the style attributes present
in it. An SVM is trained to learn attributes of the clothes
present in the user’s closet using the features generated from
a 16-layer VGGNet (our system uses the 19 layer for fash-
ioning the clothes). The test dataset is created by generated
a random combination of attributes (these combinations are
likely not present in the training image closet). For these
random combinations of attributes, the new dress images
are generated. Once featurized by a pre-trained VGG-16,
we check the SVM’s ability to predict the combinations of
The UCO images and the set of images used for styling
are maintained separately. There are a total of 400 UCOs
and 100 images from the user’s wardrobe. There are two
kinds of tests considered in the experiment. In the ﬁrst,
the test images are generated from a set of images separate
from the styles extracted from the training but with similar
attributes. In the second, the test images are generated from
the styles extracted from the training data itself. Figure
5 shows the F1-score for a varying number of test images
generated. The consistent performance above the baseline
suggests the style is likely transferred and the SVM is able
the classify based on features generated.
Our experiments with increasing the number of images
used for gaining more styles showed a drop in the F1 score,
suggesting that an increasing number of style functions im-
pact the quality of the result, thus making it diﬃcult to
identify patterns. Hence it is necessary to limit the number
of style functions used to generate the new dress.
Figure 5: Bar-chart showing F1-scores for the base-
line and our model on actual test data using separate
training and test generation images, and using same
images for training and test data generation
5.2 Qualitative evaluation
We analyze the quality of dress images by seeing how sim-
ilar they are to the style images used in the personalization
process. The quality of the generated image is impacted by
a number of factors. The eﬀect of various hyper-parameters
is measured. The Figure 4 shows an image of a sheer draped
blouse changed to adopt the styles extracted from a couple
of images. The result is a nice blend of patterns borrowed
from the style images given.
A single style superimposed on the same content image,
Figure 6: Styles extracted from multiple images for the same attribute ”knit”
but using multiple distinct style images, produces interesting
results. Figure 6 presents the style of four diﬀerent knit
garments over a tank top. Four diﬀerent textures of the
same fabric produce distinct results.
6. CONCLUSIONS & FUTURE WORK
In this paper, we show an initial pipeline to generate new
designs for clothes based on the preference of the user. The
results indicate that style transfer happens successfully and
is personalized for the closet of a user. In the future we
will like to improve the performance of the pipeline as it is
time consuming to generate a new design. Also, we plan to
experiment with better methods to personalize and generate
designs with higher resolutions.
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