Cite this paper if it is helpful, thanks!

Huang, G., Jafari, A.H. Enhanced balancing GAN: minority-class image generation. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-021-06163-8

Improved-Balancing-GAN-Minority-class-Image-Generation

Abstract

Generative adversarial networks (GANs) are one of the most powerful generative models, but always require a large and balanced dataset to train. Traditional GANs are not applicable to generate minority-class images in a highly imbalanced dataset. Balancing GAN (BAGAN) is proposed to mitigate this problem, but it is unstable when images in different classes look similar, e.g. flowers and cells. In this work, we propose a supervised autoencoder with an intermediate embedding model to disperse the labeled latent vectors. With the improved autoencoder initialization, we also build an architecture of BAGAN with gradient penalty (BAGAN-GP). Our proposed model overcomes the unstable issue in original BAGAN and converges faster to high quality generations. Our model achieves high performance on the imbalanced scale-down version of MNIST Fashion, CIFAR-10, and one small-scale medical image dataset.

Problem statement

Although BAGAN proposed an autoencoder initialization to stabilize the GAN training, sometimes the performance of BAGAN is still unstable especially on medical image datasets. Medical image datasets are always: 1. highly imbalanced due to the rare pathological cases, 2. hard to distinguish the difference among classes. As shown in BAGAN paper, the imbalanced Flowers dateset has many similar classes so that BAGAN performs not well. In our experiments, BAGAN fails to generate good samples on a small-scale medical image dataset. We consider that the encoder fails to separate images by class when translating them into latent vectors. Our objective of this work is to generate minority-class images in high quality even with a small-scale imbalanced dataset. Our contributions are:

• We improve the loss function of BAGAN with gradient penalty and build the corresponding architecture of the generator and the discriminator (BAGAN-GP).
• We propose a novel architecture of autoencoder with an intermediate embedding model, which helps the autoencoder learn the label information directly.
• We discuss the drawbacks of the original BAGAN and exemplify performance improvements over the original BAGAN and demonstrate the potential reasons.

Dataset

1. MNIST Fashion and CIFAR-10. We also create an imbalanced version for these two datasets.

MNIST Fashion	T-shirt	Trouser	Pullover	Dress	Coat	Sandal	Shirt	Sneaker	Bag	Boot
Balanced	4231	4165	4199	4211	4185	4217	4189	4241	4175	4187
Imbalanced	4166	73	139	210	287	370	422	387	545	651

• MNIST-Fashion (imbalanced-version) with BAGAN-GP

CIFAR-10	Airplane	Automobile	Bird	Cat	Deer	Dog	Frog	Horse	Ship	Truck
Balanced	3527	3523	3500	3458	3563	3455	3535	3509	3453	3476
Imbalanced	3490	71	130	221	269	349	435	485	572	628

2. Small-scale imbalanced medical image dataset: Cells. Download it with the link wget https://storage.googleapis.com/exam-deep-learning/train.zip.

Cells	red blood cell (normal)	ring	schizont	trophozoite
Train	5600	292	106	887
Test	1400	73	27	222

Networks

Some neural networks we've referred to in the work:

1. Generative Adversarial Networks (GANs): BAGAN, WGAN-GP, DRAGAN, ACGAN.
2. Autoencoder.
3. Pre-trained networks: ResNet50, Inception V3.

Framework

1. Keras
2. TensorFlow (2.2)

Metrics

Fréchet Inception Distance

FID calculates the feature-level distance between the generated sample distribution and the real sample distribution by Inception V3 network. A smaller FID means the generated distribution is closer to the real distribution.