Research map / Detection and segmentation / Open-set and few-shot detection
Where's Wally Now? Deep Generative and Discriminative Embeddings for Novelty Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use 1CSVM on the GAN latent embedding instead of GANOMALY | Generative novelty detection · CIFAR-10 (MISO) | AUC: 0.5321 (0.1292) -> 0.5627 (0.1458) | Helped |
| Use 1CSVM on the GAN latent embedding instead of GANOMALY | Generative novelty detection · CIFAR-10 (SIMO) | AUC: 0.6228 (0.1092) -> 0.6279 (0.1266) | Helped |
| Use 1CSVM on the GAN latent embedding instead of GANOMALY | Generative novelty detection · IN-125 (MISO) | AUC: 0.5708 (0.0775) -> 0.5792 (0.0719) | Helped |
| Evaluate novelty detection across problem-complexity levels using BERCA | Novelty detection evaluation · IN-125 (MISO) | AUC | Helped |
| Use KL divergence (KLCA) as the empirical complexity measure | Novelty detection evaluation · IN-125 (MISO) | correlation with detection performance | Hurt |
| Increase BERCA problem complexity | Deep-embedding novelty detection methods · IN-125 (MISO) | AUC | Hurt |
| Use LOF on GAP features as BERCA complexity increases | LOF/XGAP 512 · IN-125 (MISO) | AUC | No clear effect |
Closest papers by meaning
- Hierarchical Novelty Detection for Visual Object Recognition (CVPR 2018 · similarity 0.89)
- Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models (NeurIPS 2023 · similarity 0.89)
- Universal Novelty Detection Through Adaptive Contrastive Learning (CVPR 2024 · similarity 0.87)
- An Interpretable Evaluation of Entropy-based Novelty of Generative Models (ICML 2024 · similarity 0.85)
- Image Generation Diversity Issues and How to Tame Them (CVPR 2025 · similarity 0.85)
- Anomaly Score: Evaluating Generative Models and Individual Generated Images based on Complexity and Vulnerability (CVPR 2024 · similarity 0.84)
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