Research map / Label-efficient and robust learning / Recognition and long-tail
Partial Multi-View Outlier Detection Based on Collective Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use collective-learning missing-sample recovery with self-guided outlier detection instead of mean-imputation plus existing detectors | Partial multi-view outlier detection · Synthetic, POR 75% | AUC: 0.7811 -> 0.8055 (vs AP) | Helped |
| Use collective-learning method instead of best baseline AP | Flowers · Flowers, POR 0% | AUC: 0.8066 -> 0.8712 | Helped |
| Use collective-learning method instead of best baseline AP | USPS-MNIST · USPS-MNIST, POR 75% | AUC: 0.7470 -> 0.8123 | Helped |
| Increase partial object ratio from 0% to 75% | Ours (CL) · Synthetic | AUC: 0.9385 -> 0.8055 | Hurt |
| Unified self-guided integration versus two-step reconstruct then detect | Ours (CL) · all datasets | AUC | Helped |
| Set kNN neighbor count k to 3%-5% of objects (ablation) | Ours (CL) · Synthetic and USPS-MNIST, POR 45% | AUC | Helped |
Closest papers by meaning
- RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples (ICML 2024 · similarity 0.86)
- ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effect (AAAI 2025 · similarity 0.85)
- DOS: Diverse Outlier Sampling for Out-of-Distribution Detection (ICLR 2024 · similarity 0.85)
- Unveiling Multi-View Anomaly Detection: Intra-view Decoupling and Inter-view Fusion (AAAI 2025 · similarity 0.85)
- ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models (ICML 2024 · similarity 0.84)
- Quantile-Based Maximum Likelihood Training for Outlier Detection (AAAI 2024 · similarity 0.84)
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