Distribution Fitting for Combating Mode Collapse in Generative Adversarial Networks.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computer Vision

Background:

• Mode collapse is a critical challenge in Generative Adversarial Networks (GANs), hindering their ability to generate diverse and realistic data.
• Existing GAN training methods can fail to capture the full data distribution due to nonuniform sampling, leading to suboptimal solutions.

Purpose of the Study:

• To investigate the root causes of mode collapse in GANs from a novel perspective.
• To propose new methods for mitigating mode collapse and enhancing the stability and performance of GANs.

Main Methods:

• Introduced a Global Distribution Fitting (GDF) method with a penalty term to constrain the generated data distribution.
• Developed a Local Distribution Fitting (LDF) method to handle scenarios where the complete real data distribution is unattainable.

Main Results:

• GDF effectively penalizes generated distributions that deviate from the real data distribution without altering the original global minimum.
• LDF provides a solution for cases with unreachable real data distributions.
• Experimental results on multiple benchmarks validate the effectiveness and competitive performance of both GDF and LDF.

Conclusions:

• The proposed GDF and LDF methods offer effective solutions to the persistent problem of mode collapse in GANs.
• These novel approaches enhance the training stability and generative capabilities of GANs, paving the way for more robust deep learning models.