Auto-Encoding Generative Adversarial Networks towards Mode Collapse Reduction and Feature Representation Enhancement.

Area of Science:

• Artificial Intelligence
• Deep Learning
• Machine Learning

Background:

• Generative Adversarial Networks (GANs) are powerful deep learning tools for image, video, speech, and text processing.
• Existing GANs face challenges like mode collapse and unstable training, limiting their effectiveness.
• Addressing these limitations is crucial for advancing generative modeling capabilities.

Purpose of the Study:

• To propose a novel Auto-Encoding Generative Adversarial Network (GAN) architecture.
• To enhance feature representation and mitigate mode collapse in GANs.
• To improve the stability and performance of generative models.

Main Methods:

• Developed an Auto-Encoding GAN comprising generators, a discriminator, an encoder, and a decoder.
• Utilized generators for diverse mode learning and a discriminator for sample distinction.
• Employed an encoder-decoder for mapping samples to an embedding space and identifying sample origins.
• Integrated a clustering algorithm and cluster center matching for distribution consistency.

Main Results:

• The proposed Auto-Encoding GAN effectively reduces mode collapse.
• Enhanced feature representation capabilities were observed in the model.
• Experiments demonstrated superior performance in maintaining data distribution consistency.
• Both visual and quantitative results confirmed the model's effectiveness.

Conclusions:

• The Auto-Encoding GAN offers a robust solution to GAN training instability and mode collapse.
• The model significantly improves feature representation and distribution consistency.
• This framework presents a promising advancement for various generative AI applications.