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Neighborhood Consensus Networks for Unsupervised Multi-view Outlier Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use NCMOD instead of MUVAD-s | Multi-view outlier detection · 2-view datasets (18-dataset average) | AUC: 0.711 -> 0.791 | Helped |
| Use NCMOD instead of LDSR | Multi-view outlier detection · 2-view datasets (18-dataset average) | F1: 0.373 -> 0.498 | Helped |
| Use NCMOD instead of MUVAD-s | Multi-view outlier detection · 3-view datasets (18-dataset average) | AUC: 0.698 -> 0.764 | Helped |
| Use NCMOD instead of MUVAD-s | Multi-view outlier detection · 3-view datasets (18-dataset average) | F1: 0.334 -> 0.433 | Helped |
| Add cross-view neighborhood consensus to the autoencoder (ablation) | NCMOD · MNIST (average) | AUC: 0.629 -> 0.884 | Helped |
| Add cross-view neighborhood consensus to the autoencoder (ablation) | NCMOD · REUTERS (average) | AUC: 0.544 -> 0.834 | Helped |
| Add cross-view neighborhood consensus to the autoencoder (ablation) | NCMOD · TTC (average) | AUC: 0.547 -> 0.616 | Helped |
| Add within-view autoencoder reconstruction (ablation) | NCMOD · MNIST (average) | AUC: 0.692 -> 0.884 | Helped |
| Add within-view autoencoder reconstruction (ablation) | NCMOD · REUTERS (average) | AUC: 0.491 -> 0.834 | Helped |
| Add within-view autoencoder reconstruction (ablation) | NCMOD · TTC (average) | AUC: 0.533 -> 0.616 | Helped |
Closest papers by meaning
- ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effect (AAAI 2025 · similarity 0.85)
- ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models (ICML 2024 · similarity 0.85)
- RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples (ICML 2024 · similarity 0.84)
- DOS: Diverse Outlier Sampling for Out-of-Distribution Detection (ICLR 2024 · similarity 0.84)
- Unveiling Multi-View Anomaly Detection: Intra-view Decoupling and Inter-view Fusion (AAAI 2025 · similarity 0.84)
- Robust Multi-view Clustering against Imperfect Information (NeurIPS 2026 · similarity 0.84)
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