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How to Know When Machine Learning Does Not Now
It is becoming increasingly important to understand how a prediction made by a Machine Learning model is informed by its training data.
adversarial-learning interpretability uncertainty adversarial-examples
Generalized Zero & Few-Shot Transfer for Facial Forgery Detection
Deep Distribution Transfer (DDT), a new transfer learning approach to address the problem of zero and few-shot transfer in the context of facial forgery.
zero-shot-learning few-shot-learning facial-forgery-detection fraud-detection
Generative Adversarial Networks for Outlier Detection
PyTorch implementation of a GAN architecture for the problem of outlier detection.
generative-adversarial-networks outlier-detection pytorch anomaly-detection
Deep Learning for Anomaly Detection: A Survey
We present a structured and comprehensive review of research methods in deep anomaly detection (DAD).
anomaly-detection survey research tutorial
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