EGBAD: Efficient GAN‑Based Anomaly Detection – Theory and Practical Implementation

This article introduces the EGBAD model, an efficient GAN‑based anomaly detection method that replaces AnoGAN's costly latent variable search with an encoder, provides detailed PyTorch code for data loading, model construction, training, and inference, and compares its testing speed with AnoGAN.

DiscriminatorEGBADEncoder

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EGBAD: Efficient GAN‑Based Anomaly Detection – Theory and Practical Implementation

Code DAO

Jun 3, 2022 · Artificial Intelligence

Decomposing PointGAN: Teaching a Machine to Generate a Single Point

This article walks through building and analyzing a minimal GAN—PointGAN—that learns to output the single value 1, covering the linear generator, a two‑layer discriminator, training loops, loss visualizations, instability diagnostics, and practical fixes such as loss easing, weighted examples, weight decay, and noisy generator parameters.

DiscriminatorGANNoise Injection

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Decomposing PointGAN: Teaching a Machine to Generate a Single Point