Research map / Label-efficient and robust learning / Recognition and long-tail
Hierarchically Robust Representation Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add example-level robustness (ablation) | ERM deep features · CIFAR-10 | accuracy: 85.77±0.12 -> 86.62±0.18 | Helped |
| add concept-level robustness (ablation) | ERM deep features · CIFAR-10 | accuracy: 85.77±0.12 -> 85.64±0.26 | No clear effect |
| add both example-level and concept-level robustness | ERM deep features · CIFAR-10 | accuracy: 85.77±0.12 -> 86.49±0.19 | Helped |
| add example-level robustness (ablation) | ERM deep features · SOP | accuracy: 73.47±0.09 -> 73.48±0.08 | No clear effect |
| add concept-level robustness (ablation) | ERM deep features · SOP | accuracy: 73.47±0.09 -> 74.34±0.05 | Helped |
| add both example-level and concept-level robustness | ERM deep features · SOP | accuracy: 73.47±0.09 -> 74.23±0.08 | Helped |
| add example-level robustness (ablation) | ERM deep features · SVHN | accuracy: 63.23±0.35 -> 65.01±0.37 | Helped |
| add concept-level robustness (ablation) | ERM deep features · SVHN | accuracy: 63.23±0.35 -> 65.47±0.27 | Helped |
| add both example-level and concept-level robustness | ERM deep features · SVHN | accuracy: 63.23±0.35 -> 67.33±0.39 | Helped |
Closest papers by meaning
- Hierarchical Selective Classification (NeurIPS 2024 · similarity 0.86)
- Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners (CVPR 2024 · similarity 0.86)
- Robustness Reprogramming for Representation Learning (ICLR 2025 · similarity 0.86)
- How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model (ICML 2024 · similarity 0.86)
- LORE: Lagrangian-Optimized Robust Embeddings for Visual Encoders (NeurIPS 2025 · similarity 0.85)
- Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness (ICML 2024 · similarity 0.85)
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