Research map / Learning theory and algorithms / Submodular and game algorithms
Creating Capsule Wardrobes From Fashion Images
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use iterative layer-wise greedy selection instead of naive greedy selection (ablation) | Capsule optimization · Toy capsule optimization (N=10, T=3) | Obj./Optimal Obj. (%): 76 -> 87 | Helped |
| Use iterative layer-wise greedy selection instead of naive greedy selection (ablation) | Capsule creation · Polyvore gold-standard evaluation | Compatibility: 0.88 -> 0.83 | Helped |
| Use iterative layer-wise greedy selection instead of naive greedy selection (ablation) | Capsule creation · Human subject study | Preference for generated capsules: 59% | Helped |
| Increase MMR λ from 0.3 to 0.7, emphasizing relevance (ablation) | MMR capsule selection · Polyvore gold-standard evaluation | Compatibility: 3.05 -> 2.12 | Helped |
| Use two-stage catalog-to-outfit curriculum learning for attribute recognition | ResNet-50 attribute recognition · 19K image validation split | mAP: 15% | Helped |
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