BAGAN: Data Augmentation with Balancing GAN (1803.09655v2)

Abstract: Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers. In this work we propose balancing GAN (BAGAN) as an augmentation tool to restore balance in imbalanced datasets. This is challenging because the few minority-class images may not be enough to train a GAN. We overcome this issue by including during the adversarial training all available images of majority and minority classes. The generative model learns useful features from majority classes and uses these to generate images for minority classes. We apply class conditioning in the latent space to drive the generation process towards a target class. The generator in the GAN is initialized with the encoder module of an autoencoder that enables us to learn an accurate class-conditioning in the latent space. We compare the proposed methodology with state-of-the-art GANs and demonstrate that BAGAN generates images of superior quality when trained with an imbalanced dataset.

Overview of "BAGAN: Data Augmentation with Balancing GAN"

The research paper titled "BAGAN: Data Augmentation with Balancing GAN" addresses the critical issue of class imbalance in image classification datasets. Class imbalance is a common challenge that adversely impacts the performance of deep learning-based classifiers. Traditional data augmentation techniques, like geometric transformations, fail to effectively address this problem without risking the alteration of salient features critical for classification. To offer a robust solution, the authors introduce BAGAN, an innovative approach leveraging a Balancing Generative Adversarial Network to generate synthetic images for minority classes. This strategy aims to restore equilibrium in imbalanced datasets while ensuring the generation of high-quality and diverse samples for minority classes.

Technical Contributions

The authors present several notable contributions to the field of generative modeling and data augmentation:

1. Novel GAN Training Approach: The paper introduces a training methodology that incorporates both minority and majority class data throughout the adversarial training process. This approach ensures the capture of vital features across all classes, subsequently applying these learned features to synthesize new samples for underrepresented classes.
2. Autoencoder-Based Initialization: The introduction of an autoencoder for initializing the GAN's discriminator and generator stands out as a novel component of BAGAN. This initialization is crucial in avoiding the instability and convergence issues commonly associated with GANs. By starting from a stable point, the model better learns class distinctions in the latent space, facilitating higher fidelity image synthesis.
3. Class-Conditional Latent Space: BAGAN employs class conditioning in the latent space to steer the generative process towards specific target classes. By understanding how different classes are distributed within this space, BAGAN effectively enhances the generator's ability to produce class-specific realistic samples.
4. Empirical Evaluation: The methodology is empirically validated across several datasets, including modified versions of MNIST and GTSRB, demonstrating superior image generation quality over other state-of-the-art GAN architectures.

Numerical Results and Observations

The paper provides compelling numerical evidence showcasing BAGAN's performance improvements:

• BAGAN achieves a high classification accuracy on augmented datasets, significantly improving upon datasets that originally suffered from severe class imbalances.
• The generated images demonstrate a level of diversity that is crucial in approximating real-world sample distributions without succumbing to mode collapse.
• BAGAN outperforms baseline approaches like ACGAN, especially in generating minority-class images that exhibit both diversity and high perceptual quality.

Implications and Future Directions

The introduction of BAGAN holds substantial implications for both theoretical and practical applications. Theoretically, it enriches the understanding of integrating autoencoding frameworks within adversarial setups for improved learning and image synthesis. Practically, BAGAN could be highly beneficial in domains where data imbalance is prevalent, such as medical imaging, traffic sign recognition, and other applications where obtaining balanced datasets is inherently challenging.

Looking forward, the principles embedded in BAGAN could inspire future research in several directions. One potential area is the exploration of more nuanced autoencoder architectures that could further enhance initial conditions for GAN training. Additionally, refining the model's capacity to handle even more diversified image classes and complex datasets could extend its applicability. Another prospective research line is the integration of other machine learning models to complement the current framework and improve robustness, efficiency, and scalability.

In conclusion, BAGAN represents a pivotal advancement in the ongoing endeavor to leverage GANs for practical data augmentation, offering a pathway to more balanced and effective training datasets in numerous machine learning applications.