Research map / Label-efficient and robust learning / Domain adaptation and generalization
Style Neophile: Constantly Seeking Novel Styles for Domain Generalization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use constantly synthesized novel styles instead of limiting augmentation to known or interpolated styles (ablation) | Style Neophile with ResNet18 · PACS | average top-1 accuracy: 84.44 -> 85.47 | Helped |
| add style injection with random noise to the unaugmented baseline (ablation) | Style Neophile with ResNet18 · PACS | average top-1 accuracy: 79.94 -> 83.38 | Helped |
| add novel style synthesis to random-noise style injection (ablation) | Style Neophile with ResNet18 · PACS | average top-1 accuracy: 83.38 -> 84.44 | Helped |
| add consistency losses between original and style-injected predictions (ablation) | Style Neophile with ResNet18 · PACS | average top-1 accuracy: 84.44 -> 85.47 | Helped |
| use Style Neophile rather than the strongest listed baseline | PACS with ResNet18 · PACS | average top-1 accuracy: 84.51 -> 85.47 | Helped |
| use Style Neophile rather than the strongest listed baseline | Style Neophile with ResNet50 · PACS | average top-1 accuracy: 88.15 -> 89.11 | Helped |
| use Style Neophile rather than FACT | Style Neophile · OfficeHome | average top-1 accuracy: 66.56 -> 65.89 | Hurt |
| use Style Neophile rather than DMG | Style Neophile with ResNet18 · DomainNet | average top-1 accuracy: 39.00 -> 39.95 | Helped |
| use Style Neophile rather than the ResNet50 baseline | Style Neophile with ResNet50 · DomainNet | average top-1 accuracy: 44.00 -> 44.60 | Helped |
| use Style Neophile rather than L2A-OT | Style Neophile with OSNet-IBN · Market1501 → Duke | mAP: 29.2 -> 29.7 | Helped |
| use Style Neophile rather than L2A-OT | Style Neophile with OSNet-IBN · Duke → Market1501 | mAP: 30.2 -> 32.2 | Helped |
| increase the length of the source-style or novel-style queue within the tested ranges (ablation) | Style Neophile · PACS | average top-1 accuracy | No clear effect |
Closest papers by meaning
- DGFamba: Learning Flow Factorized State Space for Visual Domain Generalization (AAAI 2025 · similarity 0.87)
- Towards domain-invariant Self-Supervised Learning with Batch Styles Standardization (ICLR 2024 · similarity 0.87)
- StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization (ICML 2024 · similarity 0.86)
- Content-Style Learning from Unaligned Domains: Identifiability under Unknown Latent Dimensions (ICLR 2025 · similarity 0.86)
- Exploring Semantic Consistency and Style Diversity for Domain Generalized Semantic Segmentation (AAAI 2025 · similarity 0.86)
- Gradient-Guided Annealing for Domain Generalization (CVPR 2025 · similarity 0.85)
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