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Found 3 papers, 2 papers with code
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks
When training on such datasets, existing GANs will learn a mixture distribution of desired and contaminated instances, rather than the desired distribution of desired data only (target distribution).
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs
The goal of anomaly detection is to identify anomalous samples from normal ones.
Global Convergence and Stability of Stochastic Gradient Descent
Under a slightly more restrictive assumption on the joint behavior of the non-convexity and noise model that generalizes current assumptions in the literature, we show that the objective function cannot diverge, even if the iterates diverge.
