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GAN Training Analysis.pdf

2007.06418.pdf

🔗 Source: arxiv.org

📊 Size: 6.8 MB

📄 Pages: 12 pages

⬇️ Downloads: 91

Summary

GANs often struggle with underfitting or overfitting due to limited or complex datasets, making analysis difficult. Training GANs on artificial datasets with infinite samples and simple distributions reveals that generators may fail to learn optimal parameters. Increasing model complexity by training mixtures of GANs leads to better performance than increasing network depth or width, allowing generators to discover different modes or classes automatically. Experiments on artificial datasets and CIFAR-10 demonstrate the effectiveness of this approach, achieving state-of-the-art results in terms of Inception Score and Fréchet Inception Distance.

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Description

GANs often struggle with underfitting or overfitting due to limited or complex datasets, making analysis difficult.

Technical Information

File Format: PDF
File Size: 6.8 MB
Pages: 12
Language: EN
Total Downloads: 91
Last Updated: 3 hours ago

Document Overview

This PDF document about GAN Training Analysis provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into GAN Training Analysis.