Research map / Label-efficient and robust learning / Test-time adaptation
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use few-shot target data to adapt the learned embedding | DML representations · CUB200-2011 and CARS196 ooDML | Recall@1 | Helped |
| Use few-shot target data to adapt the learned embedding | DML representations · CUB200-2011 and CARS196 ooDML | mAP@1000 | Helped |
| Increase train-test distribution shift | DML methods · ooDML benchmarks | Recall@1 | Hurt |
| Use semantic feature diversity and self-distillation | DML methods · CUB200-2011, CARS196, and SOP ooDML | Aggregated Generalization Score | Helped |
| Increase backbone capacity | DML models · ooDML benchmarks | Recall@1 | Mixed |
| Use generic pretrained representations without benchmark adaptation | Generic representations · CUB200-2011, CARS196, and SOP | Recall@1 | Mixed |
| Evaluate on multiple train-test shifts instead of one fixed split | DML evaluation · CUB200-2011, CARS196, and SOP | Recall@1 | Helped |
| Use structural representation properties as predictors across varying shifts | DML representations · ooDML benchmarks | correlation with Recall@1 | No clear effect |
Closest papers by meaning
- Revisiting Training Strategies and Generalization Performance in Deep Metric Learning (ICML 2020 · similarity 0.91)
- Towards Robust Out-of-Distribution Generalization Bounds via Sharpness (ICLR 2024 · similarity 0.86)
- Input Margins Can Predict Generalization Too (AAAI 2024 · similarity 0.85)
- Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization (NeurIPS 2024 · similarity 0.85)
- Rethinking the Evaluation Protocol of Domain Generalization (CVPR 2024 · similarity 0.85)
- Deep Disentangled Metric Learning (AAAI 2025 · similarity 0.85)
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