Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data

arXiv:2405.18929 · stat.ML, cs.AI, cs.LG · Submitted 2024-05-29 · Read on arXiv

stat.ML, cs.AI, cs.LG

Submitted: 2024-05-29

Updated: 2026-09-24

Terminology

• AUROC — 11× in this paper · explained in 2 episodes
• Loss Function — 10× in this paper · explained in 2 episodes
• Objective Function — 5× in this paper · explained in 2 episodes
• Reconstruction Error — 5× in this paper · explained in 2 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Stochastic Gradient Descent (SGD) — 1× in this paper · explained in 5 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• WAIC, but Why? Generative Ensembles for Robust Anomaly Detection
• Do Deep Generative Models Know What They Don't Know?
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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