Fake CLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs

Key Takeaways

• Hierarchical topic modeling and Neural Hierarchical Topics Models (NHTMs) are gaining popularity due to their ability to efficiently mine implicit semantics and construct topic hierarchical relationships


• Generative Adversarial Network (GAN) based architectures have outperformed frameworks based on Neural Variational Inference (NVI) in generating higher-quality topics


• Contrasting with traditional Euclidean-based models, Hyperbolic space has been introduced to model complex patterns like hierarchical structures and express hierarchical relations more rationally


• Contrastive learning has demonstrated effectiveness in mitigating the problem of pattern collapse in GANs and improving the generative performance of models like FakeCLR


• There is a demand to improve the modeling of hierarchical relationships in topic models by introducing contrastive learning and leveraging hyperbolic space

Identifying the Latent Space Discontinuity Problem

Our examination of contrastive learning (CL) strategies in data-efficient Generative Adversarial Networks (DE-GANs) uncovers a critical hurdle in their generative prowess - the discontinuity of the latent space. The generator in DE-GANs, constrained by limited training data, tends to merely recall discrete samples. This results in a discontinuous latent space and under-diverse sample generation.

Instance-Perturbation: The Key to Improving Generative Performance

Instance-Perturbation emerges as the most impactful strategy among the three CL approaches for enhancing data-efficient GANs (DE-GANs). Our analysis shows it effectively mitigates the latent space discontinuity issue, a major hurdle in training GANs with limited data.

Analyzing the Impact of Instance-Perturbation

The instance-perturbation technique involves applying small, semantically-preserving transformations to fake samples generated by DE-GAN models. This process bridges the gap between real and fake data distributions, ensuring a continuous and smooth latent space.

Enhancing Latent Space Continuity with Instance-Perturbation

Incorporating instance-perturbation has significantly improved latent space continuity, directly enhancing generative performance. Introducing generalized transformations produces images semantically similar to originals, aiding in representation learning.

Introducing FakeCLR: A Novel Contrastive Learning Approach

FakeCLR introduces several key innovations:

Noise-Related Latent Augmentation

FakeCLR introduces a unique technique called Noise-Related Latent Augmentation. This technique enriches the latent space with task-relevant noise. It expands the latent manifold, leading to more stable and diverse sample generation.

Diversity-Aware Queue

The Diversity-Aware Queue is another innovation in FakeCLR. It acts as a dynamic memory bank for diverse fake samples. This mechanism ensures that the contrastive learning process encompasses a broad range of samples.

Forgetting Factor of Queue

FakeCLR also incorporates a Forgetting Factor. This factor gradually removes older samples from the queue. It allows the model to adapt to changing data distributions, keeping the contrastive learning focused on the most relevant samples.

Experimental Validation and Results

Theoretical Analysis and Insights

Conclusion

This paper delves into the core challenge of latent space discontinuity in Data-Efficient Generative Adversarial Networks (DE-GANs). We introduce FakeCLR, a novel contrastive learning approach, to tackle this problem. Our experiments demonstrate FakeCLR's superiority, outperforming current DE-GAN methods in few-shot and limited-data generation tasks.

FAQ

What are Generative Adversarial Networks (GANs)?

Generative Adversarial Networks (GANs) represent a sophisticated machine learning approach. They are capable of producing images that are indistinguishable from authentic ones.

What are Data-Efficient GANs (DE-GANs)?

Data-Efficient GANs (DE-GANs) are designed to construct generative models with minimal training data. This is essential for numerous practical applications.

What challenges do DE-GANs face?

DE-GANs encounter several hurdles. These include training instability caused by discriminator overfitting and the creation of low-quality, under-diverse samples.

How can contrastive learning improve the synthesis quality of DE-GANs?

Contrastive learning (CL) has demonstrated significant potential in enhancing DE-GANs' synthesis quality. However, the underlying motivations and principles of various CL strategies remain unexplored.

What is the key bottleneck of DE-GANs?

The primary limitation of DE-GANs lies in the discontinuity of their latent space. This discontinuity results in the generation of under-diverse samples.

What is the proposed FakeCLR method?

FakeCLR represents a novel contrastive learning approach. It is specifically tailored to elevate DE-GANs' performance by addressing the latent space discontinuity issue.

What are the key components of FakeCLR?

FakeCLR introduces three groundbreaking training techniques. These include Noise-Related Latent Augmentation, Diversity-Aware Queue, and Forgetting Factor of Queue. These innovations aim to enhance the continuity of the latent space and the diversity of generated samples.

How does FakeCLR perform compared to other DE-GAN methods?

Extensive experiments reveal that FakeCLR surpasses existing DE-GAN methods in both few-shot generation and limited-data generation tasks. It achieves state-of-the-art performance, significantly outdoing its predecessors.