Application of GAN for Reducing Data Imbalance under Limited
Gaurav Adke
Michelin India Private Limited, Pune, India
Keywords: Generative Adversarial Networks, Non-conformity Diagnosis, Unbalanced Dataset, Data Augmentation.
Abstract: The paper discusses architectural and training improvements of generative adversarial network (GAN) model
for stable training. The advanced GAN architecture is proposed combining these improvements and it is
applied for augmentation of a tire joint nonconformity dataset used for classification applications. The dataset
used is highly unbalanced with higher number of conformity images. This unbalanced and limited dataset of
nonconformity identification poses challenges in developing accurate nonconformity classification models.
Therefore, a research is carried out in the presented work to augment the nonconformity dataset along with
increasing the balance between different nonconformity classes. The quality of generated images is improved
by incorporating recent developments in GANs. The present study shows that the proposed advanced GAN
model is helpful in improving the performance classification model by augmentation under a limited
unbalanced dataset. Generated results of advanced GAN are evaluated using Fréchet Inception Distance (FID)
score, which shows large improvement over styleGAN architecture. Further experiments for dataset
augmentation using generated images show 12% improvement in classification model accuracy over the
original dataset. The potency of augmentation using GAN generated images is experimentally proved using
principal component analysis plots.
Deep learning algorithms in computer vision domain
can get highly suffered with limited data. An accuracy
of the deep learning model can get further degraded
with imbalance dataset. Nonconformity detection in
an automated inspection process is a task where the
model needs to identify nonconforming samples in
input images and classify them as per the class of the
nonconformities. Collection of a dataset to train such
model is a time-consuming process, as the samples
are needed to be acquired from the relevant inspection
line over the period of time. Another limitation of this
collected dataset is that it can be highly imbalanced
with a large number of samples of a normal or
conforming class. This is obvious since any
production line is designed to produce conforming
samples. It is highly impractical and expensive to
generate conforming samples from the production
line to balance the dataset.
Standard image augmentation techniques have
been developed to enhance the available dataset.
These techniques apply label invariant and
semantically preserving transformations to original
images. Examples of such techniques are zooming in
and out, random flips, random shifts, rotations,
brightness variations etc. (Shorten and Khoshgoftaar,
2019). Since augmented images are in general mere
modifications of real images, they are of limited help
to capture complete probability distribution of input
dataset (Antoniou et al., 2017). Moreover, application
of these techniques is problem dependent.
Considering these limitations of standard
augmentations and the requirement to improve
accuracy of classification models for nonconformity
detection tasks, generative adversarial networks
(GAN) (Goodfellow et al., 2014) are studied to tackle
data augmentation challenges. GANs are primarily
trained with the implicit objective of capturing a
distribution of real data. This property of GAN is
particularly beneficial for augmentation tasks as
generated samples would cover maximum underlying
distributions of real datasets. It can also lead to
reduced overfitting in the classification model (Zhao
et al., 2020b).
The research work presented in this paper
describes exploration of recent state-of-the-art
improvements in GAN algorithms to tackle low and
Adke, G.
Application of GAN for Reducing Data Imbalance under Limited Dataset.
DOI: 10.5220/0010782800003124
In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP, pages
ISBN: 978-989-758-555-5; ISSN: 2184-4321
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
unbalanced datasets at hand. These improvements
cover changes in GAN architecture, loss function,
data augmentation, regularization techniques. The
work is focused on capturing fine details in generated
images with larger variations. This objective is
particularly challenging for a low number of training
The paper is organized as follows. Section 2
describes methodologies used to improve baseline
StyleGAN architecture. Details of experiments, with
proposed advanced GAN used to generate
augmentation images, are presented in section 3.
Section 4 concludes the article. To the best of the
author’s knowledge, this study is a first attempt to
incorporate recent developments in generative
adversarial networks to tackle data imbalance issues
in low dataset scenarios.
Generative models such as Generative Adversarial
Networks (GAN) are capable of generating sample
images which follow similar distributions as the input
real dataset (P
) (Goodfellow et al., 2014). GAN is
a deep neural network-based model, primarily used
for creating synthetic images following a distribution
of the training data. Basic architecture of GAN is
shown in figure 1 below. It contains two models:
Generator and Discriminator. The main objective of
the generator model is to learn to match the
distribution of real data and create samples similar to
it. On the other hand, the discriminator tries to judge
the samples provided to it as real or fake.
A noise vector is used as an input to the generator
for creating new samples. This noise is drawn from
random normal distribution. The generator learns to
map normal noise to features in output images. Both
generator and discriminator models are modelled as
convolution neural networks for image generation
tasks (Radford et al., 2016). The generator has up-
convolution layers which output images given the
noise vector as input, whereas the discriminator has
down-convolution layers which outputs a probability
for the input being real. GAN training is an
adversarial fight between generator and
discriminator, where each one tries to defeat the
other. Eventually the discriminator gets better in
identifying real and fake samples; and the generator
gets better in creating samples which are difficult to
be distinguished from the real ones by the
Since the introduction of GAN in 2014, many
studies have attempted to use GAN for data
generation tasks (AlQahtani et al., 2019). Aggarwal
et al ((Aggarwal et al., 2021) have reviewed
applications of GAN in augmentation of medical and
pandemic applications. It is presented that fake image
generation using GAN can help to increase datasets
along with preserving privacy of patients and
reducing extra cost of medical imaging processes.
Gao et al (Gao et al., 2020) have used GAN for
augmenting machine nonconformity diagnostic
datasets. They have demonstrated improvements in
classifier accuracy with GAN generated datasets.
GAN is used for anomaly detection by Ackey et al
(Akcay et al., 2018). For identifying abnormal/
nonconforming samples, their model has resulted in
92% of area under the curve of the receiver operating
characteristics curve. Ma et al. (Ma et al., 2020) have
explored 3D generation capabilities of GAN for
labelled dataset augmentation for Augmented Reality
applications. Many interesting applications of GAN
have been explored by researchers in the areas of
image preprocessing, inpainting, super resolutions,
image background domain change etc (Li and Wand,
2016; Pathak et al., 2016; Ledig et al., 2017; Taigman
et al., 2017).
Various studies have been carried out to
understand GAN training behavior and improve its
stability and output quality. (Karras et al., 2018;
Karras et al., 2019; Karras et al., 2020b) have
researched upon generating high resolution images
with improved images quality. They have achieved an
FID score as low as 2.84 for FFHQ dataset (Karras et
al., 2019) and 2.32 for LSUN car dataset (Kramberger
and Potocnik, 2020). The styleGAN architecture was
extended to use label conditioning during generation
by Oeldorf et al (Mirza and Osindero, 2014; Oeldorf
and Spanakis, 2019). A labelled image dataset is used
to train conditional GAN while the generator is fed
with random labels along the noise vector during
training. They could achieve an FID score of 101.9
when trained as a conditioned dataset. GAN training
stability is an active area of research with numerous
works carried out on regularizing techniques (Lee and
Seok, 2020; Kurach et al., 2019). Zhang et al (Zhang
et al., 2020) proposed consistency regularization for
trained GAN, where the discriminator is regularized
to produce consistent predictions for similar images
with semantic preserving augmentations. This
ensures that the discriminators focus on structural
details in images and better gradient flows to the
generator. Mescheder et al (Mescheder et al., 2018)
Application of GAN for Reducing Data Imbalance under Limited Dataset
Figure 1: Basic GAN model is shown with example image
taken from CelebA dataset (Liu et al., 2015).
have proposed a gradient-based penalty for the
discriminator to ensure it follows Lipschitz
continuity. This helps in producing a smoother
prediction landscape for the discriminator with small
steps of gradient for better convergence. Karras et al
(Karras et al., 2020b) suggested to regularize the
generator with perceptual path length. This ensures
untangled and smoother mapping of latent vector to
image features. Various research is focused on
challenges of low training data by augmentation
(Zhao et al., 2020a; Karras et al., 2020a; Sinha et al.,
2021) and regularization (Tseng et al., 2021). These
are discussed with further details in the methodology
The main objective of presented work is to produce
good quality images of nonconformities, which will
be helpful for the downstream task of image
classification. GAN architecture used for the current
task is based on StyleGAN proposed by Karras et al
(Karras et al., 2019). The following GAN model and
training improvements are incorporated during the
current study.
3.1 StyleGAN
StyleGAN is an extension of progressive GAN
architecture proposed by same authors (Karras et al.,
2018). Progressively growing the generator helps to
produces high resolution images with improved
quality. It segregates low level features training from
high level training, thus capturing fine details in high
resolution images. StyleGAN appends the mapping
network to the progressive network. The mapping
network is used to transform input latent noise into
intermediate vectors. This helps in reducing
entangled features in generated images. These
intermediate vectors are injected in the generator
network at different stages to have better control on
generated images. The injection happens through
Adaptive Instance Normalization (AdaIN)
layers to match the style of generator feature maps
as per input vector. Stochastic variation in output
images is achieved by adding random noise at each
stage. The discriminator network is a mirror copy of
the generator where image size is progressively
reduced. Style mixing regularization is performed by
injecting different noise vectors at various stages of
the generator. An overview of StyleGAN is shown in
figure 2.
Figure 2: StyleGAN model with progressive generator and
mapping network. Layers “A” are affine transformation and
layers “B” are noise scaling operations.
3.2 U-NET Discriminator
The discriminator used in StyleGAN architecture
classifies the global image as real or fake. Hence the
loss gradients produced are of limited use to generate
locally coherent structures in images. Schoenfeld et
al. (Schonfeld¨ et al., 2020) have proposed a U-Net
based discriminator. A schematic of this architecture
is shown in Figure 3 below.
Figure 3: U-net GAN model.
The U-net GAN is capable of providing both
global and pixel level feedback to train the generator.
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
AN encoder model of the discriminator provides
global level information of input images, while a
decoder model provides per-pixel information. Per-
pixel information is useful for generating images with
semantic relatedness as per real distribution as well as
capturing fine intricate details in images as observed
in our study. Skip connections between the encoder
and decoder models transfer both high-level and low-
level details of images.
The StyleGAN architecture model developed for
the study is extended to incorporate the U-net structure.
The discriminator of StyleGAN and the loss functions
were modified accordingly as per U-net GAN. The
generator of the architecture remains unchanged.
3.3 Data Augmentation in Training
GAN-generated image quality can significantly
deteriorate with a limited amount of training data. The
discriminator may easily overfit by memorizing the
salient features from the training dataset, whereby it
stops providing meaningful gradients back to train the
generator. This leads to poor quality of generated
images and mode collapse (Bau et al., 2019). In
literature, lots of studies are carried out to apply
augmentation for training GAN (Karras et al., 2020a).
When the conventional data augmentation is applied
only to real images, the generator may produce
samples similar to real, as well as transformed, images.
This leads to undesirable distributions in generated
samples. Instead, augmentation can be applied to both
real and generated images. This would result in a
discriminator which is better in classifying augmented
images only. Consequently, it may not properly
identify non-augmented generated images due to
disconnected gradient flows after transformations.
A solution to this is the use of differential
augmentation (Zhao et al., 2020a; Karras et al.,
2020a). As the name suggests, all transformations
performed on both real and fake images are
differentiable, which helps in uninterrupted passing
of gradients from the discriminator to the generator.
This by and large trains the discriminator to identify
unaltered images from the desired target distribution
and maintains a precise training process for the
generator. Differentiability of augmentations is
achieved by using standard primary operations
offered by deep learning frameworks.
Karras et al. (Karras et al., 2020a) have studied
types of transformations which do not cause leaking
in generated images. Their results show that using
invertible transformations like pixel blitting,
geometric, and color transforms have an improved
effect on generated images in terms of measurement
metrics. These transformations are applied with
nonzero probability (preferably lower than 0.8) to use
non-augmented images as well during the training.
3.4 Loss Functions
The selection of loss function in the current study is
mainly governed by the presence of mode collapse in
generated images. Mode collapse is a situation where
the discriminator is overfitted to few features in real
image distributions. Hence, the generator tends to
produce images which are only suitable in fooling the
discriminator on those features. Consequently, the
generator loses the capability to produce variations in
the images. In the presence of limited data, the
possibility of mode collapse increases. This issue is
mainly tackled by use of Wasserstein loss with
gradient penalty (Gulrajani et al., 2017) (WGAN-
GP). It trains the discriminator to reduce Wasserstein
distance between generated distribution (P
) of
produced samples and real distribution (P
) of real
samples. WGAN-GP loss term is also appended with
a consistency term (Wei et al., 2018) to enforce
Lipschitz continuity near real data manifold.
Wasserstein loss is implemented in non-saturating
form (Goodfellow et al., 2014) as mentioned below.
Critic (discriminator) loss:
Generator loss:
In WGAN-GP, the discriminator is referred to as
“critic”, since it does not classify images as being
fake or real. Critic gives a score for images as being
real or fake. Here, critic is required to follow 1-
Lipschitz continuity to make sure a loss evaluated on
critic output follows Wasserstein distance metric
(Gulrajani et al., 2017). Use of the gradient penalty as
given by equation below, enforces Lipschitz
continuity by making norm of gradients of critic
output with respect to an input less than one.
Gradient Penalty term:
Consistency term:
Total critic loss is formulated as below:
  𝜆
∗𝐺𝑃 𝜆
Application of GAN for Reducing Data Imbalance under Limited Dataset
Here, 𝝀 and 𝝀
are scaling factors for gradient
penalty term and consistency term respectively. It is
recommended by authors to scale GP term by a value
of 10 and CT term by 2 in critic loss calculation.
3.5 Regularizations
Regularizing techniques are used in GAN training for
improving stability and convergence. These methods
can be subdivided based upon their implementation
on weights of network, their gradients and layer
outputs. A majority of regularizing techniques is
applied on the discriminator (Lee and Seok, 2020).
Very few techniques like perceptual path length
regularization are applied on generator weights
(Karras et al., 2020b). Current work focuses on
regularizing the discriminator mainly for training
stability and alleviating the mode collapse issue.
Consistency regularization (Zhao et al., 2020b) is
applied to the discriminator to impose equivariant
behaviour for applied differential augmentation. It is
applied through CutMix augmented images
(Schonfeld¨ et al., 2020). These images are created by
merging crops of real and fake images. The
consistency loss term, as given in equation 6, ensures
that the difference between a discriminator prediction
for CutMix image and a mix of predictions of its
independent crops is minimal. This loss term is added
in WGAN-GP loss mentioned above.
𝐷𝐶𝑢𝑡𝑀𝑖𝑥𝑥, 𝐺
Gradient penalty terms, as described in the
previous section and as incorporated in loss
evaluations, also provide a regularizing effect by
keeping gradient under unity and applying Lipschitz
continuity. During training, the exponential weight
averaging track of the generator weights is saved.
While generating images for augmentation, these
averaged weights are used. It produces better quality
images, as averaged weights are insensitive towards
outlier and noisy iterations during training.
The current study on image augmentation using
GAN generation utilizes the above-mentioned
improvements to produce better quality images. A
discriminator of a styleGAN model is modified to U-
NET architecture to capture pixelwise details.
Differential augmentation is implemented to address
low training dataset availability. An improved
WGAN-GP loss term is used to reduce the mode
collapse issue and generate images with increased
variations. A regularization effect is achieved by
adding consistent loss term and gradient penalty term
in loss evaluations. Finally, the generator with
exponential moving averaged weights is used to
generate images for augmentation. Hereafter, this
improvised GAN architecture is referred as Advanced
GAN in the remaining article.
The applicability of the proposed advanced GAN is
evaluated using a tire joint conformity dataset.
Images are generated using multiple experiments
with combinations of nonconforming and conforming
images from the dataset. Data augmentation is carried
out in three approaches. The summary of all
approaches followed for image generation is given in
Table 1. In the first approach, an individual GAN
model is trained for each nonconformity category.
Then these trained models are used to generate
augmented images of each nonconformity
independently. In the second approach, a GAN model
is trained on images from all categories. Augmented
images are produced using style merging on the
trained generator (Karras et al., 2019). Latent vectors
of two different nonconforming images are injected
at different resolutions of the styleGAN generator.
This way of style injection produces images changing
from nonconformity to another. Consequently, we
can have a dataset where we can convert an image
from one nonconformity category to another. The
third approach trains a separate GAN model on a set
of normal images and nonconforming images of a
single category. This model can be used to insert the
nonconformity, with which it is trained, into a normal
image by using style merging. Latent vector
interpolation is also used with the second and third
approach of data augmentation for transition image
generation from one category to another.
The proposed advanced GAN algorithm is
developed in Python 3.6 with TensorFlow 2.1.0
framework. The training of all models is carried out
in Microsoft Azure Machine Learning Services.
Single NVIDIA Tesla K80 GPU is used for
computation. The final size of images generated is
256x256 pixels. Quality of generated images is
evaluated using Fréchet inception distance (FID)
(Heusel et al., 2017a). The effectiveness of
augmentation is checked using a classification model
trained to classify images either from each
nonconformity or conformity (OK) category. The
classification model is a convolution neural network-
based model.
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
Table 1: Description of different approaches followed for data augmentation.
Generation Methods
Approach Description
Single noise
1 Individual GAN model for each defect
2 Single GAN model for all defective images only
 
Separate GAN model for each defect and normal
 
The proposed advanced GAN model is compared
with basic styleGAN model architecture. Their
performance is evaluated using FID. Note that a lower
FID score is related to better generated image quality
and improved variation. Both architectures are trained
on the same tire joint nonformity datasets and results
are compared. Table 2 shows their comparison.
Table 2: Performance comparison of StyleGAN
(Karras et al., 2018) and proposed Advanced GAN
(*NC – Nonconformity).
FID Scores
NC 1 NC 2 NC 3
StyleGAN 165.6 162 161.1
96.3 93.8 95.7
These results show a large improvement in the
FID score for advanced GAN as compared to the
styleGAN model. Results also show the usefulness of
advanced GAN in improving generation quality
under a limited number of images available for
training. An improvement in the results is contributed
by architectural and training changes carried out in
Advanced GAN. Implementation of differential
augmentation and consistency regularization has
helped in tackling limited dataset regimes. It also
stabilizes training for better convergence. The UNET
discriminator provides pixelwise feedback which
helps in improving generated image quality and hence
helps in reducing the FID score. Exponential weight
averaging of the generator weights further reduces the
FID score by smoothening training oscillations and
diminishing outlier noisy iterations.
To study the consequence of augmentation,
initially the classifier model is trained on all real
images without any GAN generated images. Standard
augmentations like horizontal flip, crop and translate
are used in classifier model training for all
experiments. The classifier model is tested on real
images only, extracted randomly from the original
dataset. Real images are split by 10% for testing and
90% for training and validation. Comparison of
different experiments on augmentation is done using
accuracy of the trained classifier model. Accuracy is
evaluated on a test dataset and reported as an average
of all test samples over all classes. Table 2 provides
details of all experiments carried out using generated
images along with real images. Results presented here
are averaged over multiple classification models
trained on the same dataset to reduce variance.
The dataset used for this study is collected in two
stages from a production line. In the first stage, a total
of 1183 samples were collected. In the second stage,
1108 additional samples were collected, making the
total count 2291 samples. GAN models are initially
trained on the first stage real dataset and generated
images are used for augmentation. Later, all real
images from both stages are used for the training of
GAN. The effectiveness of augmentation is evaluated
separately for each set of generated images from the
two stages.
All approaches presented in Table 1 are used to
generate images for each stage. Experiments in Table
3 indicate that augmentation by GAN produced images
has always enhanced the performance of the
classification model. In the first stage of dataset
collection, classification accuracy was too low due to
insufficient data. Even in this low dataset scenario, the
advanced GAN architecture presented here was able to
get trained with sufficient convergence and helped in
improving classification accuracy by augmentation.
Classification accuracy of the increased dataset of the
second stage was further enhanced by images
generated using real images from both stages.
The effectiveness of GAN augmentation is
visualized using Principal component analysis (PCA)
Application of GAN for Reducing Data Imbalance under Limited Dataset
Table 3: Evaluation details of classification model with original and augmentation datasets.
Classification Accuracy
v01 Stage 1 real dataset
v02 Stage 1 GAN generated images augmentation
v03 Stage 2 real dataset
v04 Stage 2 real + stage 1 GAN generated images
v05 Stage 2 real + stage 2 GAN generated images augmentation
v06 Stage 2 real + all generated images augmentation
Figure 4: PCA scatter plots of top two principal components for real images.
Figure 5: PCA scatter plots of top two principal components for augmented image dataset.
in Figures 4 and 5. They show distribution of
nonconforming images and conforming images in
two dimensions. The top two principal components
from PCA are plotted against each other for image
samples. Figure 4 (A) shows comparisons of each
class with the other for real images, while Figure 4
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
(B) shows a plot of distribution of all classes together.
Similarly Figure 5 (A) shows comparison of class-
wise PCA plots and Figure 5 (B) shows distribution
of all classes for real images augmented with GAN
generated images.
PCA plots of real images, as seen in Figures 4 (A)
and (B), show that different nonconformity categories
are difficult to distinguish from conforming images
and other nonconformities. When the dataset is
balanced by augmentation using GAN, as seen in
Figures 5 (A) and (B), the PCA plot shows improved
distinction between different image categories. From
this visualization it can be asserted that lack of data
leads to reduced generalization capabilities of the
classification model in capturing overall distribution
of the input data domain. This also results in lower
performance of the image classification task. GANs
are trained to capture implicit distribution of the input
data on which it is trained. Accordingly, GAN
generated augmentation images can be used to
facilitate the classification model in capturing the
input data distribution in an improved manner, thus
improving its prediction accuracy and generalization
towards unseen samples extracted from a sample
space having same distribution.
The paper discusses incorporation of recent
developments in GAN models for better generated
image quality. Proposed advanced GAN architecture
produces much lower FID scores than styleGAN,
which indicates improved image quality and variation
in generation. Various architectural and training
improvements discussed in this article are useful for
smoother convergence of GAN training. Hence
proposed advanced GAN can generate varied images
with fine details captured. Advanced GAN is
particularly useful in situations of augmentation of
limited and unbalanced datasets. An augmented
balanced dataset has shown good improvement in
accuracy of downstream tasks of image classification.
Principal component analysis of the augmented
dataset experimentally proves that generated images
from proposed advanced GAN can be helpful to
improve the distinction among different classification
Experiments presented in this study were limited
to images of size 256x256 pixels due to constraints of
computing power and processing time. Effectiveness
of augmentation by GAN generated images is high in
case of smaller datasets. Its usefulness for large
datasets needs to be studied as further work. Future
scope of the present work involves incorporating
GAN model improvements with styleGAN2 (Karras
et al., 2020b) architecture and use style merged
images for augmentation. Classwise augmentation
can be tried for classes with worse classification
I am thankful to Mr. Suhas Bindu for providing a
relevant dataset which was used in developing and
improving GAN algorithms. I am also thankful to Mr.
Himanshu Pradhan and Mr. Saurabh Gupta for their
valuable inputs during the current study. I would like
to appreciate efforts taken by Ms. Kelly Merkel for
organizing smooth content flow along with
suggesting grammatical corrections in this article.
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