Visual Tripwires: Anticipating Failure in Deep Vision Systems

arXiv:2609.28099 · cs.CV, cs.LG · Submitted 2026-09-23 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Distribution Shift — 12× in this paper · explained in 5 episodes
• Latent Representation — 12× in this paper · explained in 2 episodes
• Attention Entropy — 9× in this paper · explained in 2 episodes
• Latent Representations — 7× in this paper · explained in 3 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Calibration — 3× in this paper · explained in 7 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Monte Carlo dropout — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abstention — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptive Response — 1× in this paper · explained in 1 episode
• Aggregation Function — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Concept Drift — 1× in this paper · explained in 4 episodes
• Deferral — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
• A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
• Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
• Tent: Fully Test-time Adaptation by Entropy Minimization

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