When Does Unsupervised Learning Succeed or Fail? A PoS Perspective on Reconstruction-Based Anomaly Detection

arXiv:2609.28832 · cs.LG · Submitted 2026-09-23 · Read on arXiv

cs.LG

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• AUC — 37× in this paper · explained in 2 episodes
• Reconstruction Error — 26× in this paper · explained in 2 episodes
• Sparsity — 20× in this paper · explained in 4 episodes
• Fidelity — 19× in this paper · explained in 2 episodes
• Latent Space — 13× in this paper · explained in 7 episodes
• Latent Representation — 10× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 6× in this paper
• Representation Learning — 5× in this paper · explained in 2 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Latent Representations — 3× in this paper · explained in 3 episodes
• Pursuit of Subspaces — 3× in this paper
• Benchmarking — 2× in this paper · explained in 3 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• IEEE International Conference on Data Mining — 1× in this paper
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Linear Probing — 1× in this paper · explained in 2 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Learning Not to Reconstruct Anomalies
• Autoencoders for Anomaly Detection are Unreliable
• Auto-Encoding Variational Bayes
• SPG: Sparse-Projected Guides with Sparse Autoencoders for Zero-Shot Anomaly Detection
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Axiomatizing Neural Networks via Pursuit of Subspaces

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