Few-shot image generation with reverse contrastive learning.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Generative models like Generative Adversarial Networks (GANs) excel at generation tasks but require large datasets.
• Limited training data severely degrades the quality and diversity of generated images.
• Few-shot learning settings pose significant challenges for generative model performance.

Purpose of the Study:

• To introduce Reverse Contrastive Learning (RCL), a novel approach for high-quality and diverse image generation in few-shot scenarios.
• To develop a regularization technique that leverages latent feature information without auxiliary data or augmentations.

Main Methods:

• Proposed a novel regularization strategy based on correlations between generated samples.
• Utilized latent feature information across different sample levels effectively.
• Designed a method that does not require auxiliary information or augmentation techniques.

Main Results:

• Demonstrated superior performance compared to State-Of-The-Art (SOTA) methods in few-shot image generation.
• Showcased competitive results in low-shot learning settings.
• Validated the effectiveness of RCL through extensive qualitative and quantitative evaluations.

Conclusions:

• Reverse Contrastive Learning (RCL) effectively addresses the limitations of generative models in few-shot settings.
• The proposed two-sided regularization significantly enhances image generation quality and diversity.
• RCL offers a promising direction for generative modeling with limited data.