Revisiting GAN with Bayes-Optimal Discrimination

arXiv:2510.25609 · cs.LG, cs.AI, eess.SP · Submitted 2025-10-29 · Read on arXiv

cs.LG, cs.AI, eess.SP

Submitted: 2025-10-29

Updated: 2026-10-22

Code: https://github.com/mseitzer/pytorch-fid

Terminology

• Generator — 55× in this paper · explained in 2 episodes
• Gradient Penalty — 16× in this paper
• Spectral Normalization — 9× in this paper
• Benchmark — 7× in this paper · explained in 2 episodes
• Regularization — 6× in this paper · explained in 3 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Hyperparameters — 5× in this paper · explained in 2 episodes
• Bayes Optimal Learning Threshold — 4× in this paper
• International Conference on Machine Learning — 4× in this paper
• Embeddings — 3× in this paper · explained in 2 episodes
• Monotonicity — 3× in this paper · explained in 2 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Evaluation Protocol — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Fréchet Inception Distance (FID) — 2× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 2× in this paper
• Inception Score — 2× in this paper
• Total Variation (TV) distance — 2× in this paper · explained in 2 episodes
• Training Objective — 2× in this paper · explained in 3 episodes
• Training Objectives — 2× in this paper · explained in 2 episodes
• Wasserstein Distance — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Data Augmentation — 1× in this paper · explained in 10 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Feedback Loop — 1× in this paper · explained in 2 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Hinge Loss — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Residual Connections — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 6 episodes
• Score Function — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• BEGAN: Boundary Equilibrium Generative Adversarial Networks
• On Convergence and Stability of GANs
• Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Data

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