Research map / Label-efficient and robust learning / OOD and anomaly detection
Adversarially Learned One-Class Classifier for Novelty Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add R refinement at test time before D (ablation) | Adversarially Learned One-Class Classifier · Caltech-256, 1, 3, and 5 inlier categories | F1 | Helped |
| Add R refinement at test time before D (ablation) | Adversarially Learned One-Class Classifier · Caltech-256, 1, 3, and 5 inlier categories | AUC | Mixed |
| Add R refinement at test time before D (ablation) | Adversarially Learned One-Class Classifier · UCSD Ped2 | EER | Helped |
| Train with 10% outlier examples mixed into the training data | Adversarially Learned One-Class Classifier · UCSD Ped2 | EER: 1.3% | Hurt |
| Use only spatial patch features rather than spatial and temporal features | Adversarially Learned One-Class Classifier · UCSD Ped2 | EER | Hurt |
Closest papers by meaning
- Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm (CVPR 2020 · similarity 0.92)
- OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations (CVPR 2019 · similarity 0.91)
- Generative Probabilistic Novelty Detection with Adversarial Autoencoders (NeurIPS 2018 · similarity 0.90)
- Deep Transfer Learning for Multiple Class Novelty Detection (CVPR 2019 · similarity 0.89)
- Hierarchical Novelty Detection for Visual Object Recognition (CVPR 2018 · similarity 0.88)
- Contamination-Resilient Anomaly Detection via Adversarial Learning on Partially-Observed Normal and Anomalous Data (ICML 2024 · similarity 0.86)
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