Research map / Label-efficient and robust learning / Recognition and long-tail
Incorporating Label Embedding and Feature Augmentation for Multi-Dimensional Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add LEFA label-embedding feature augmentation to BR | BR · Flare2 | Hamming Accuracy: .922 -> .938 | Helped |
| add LEFA augmentation to ECC | ECC · Flare1 | Hamming Accuracy: .917 -> .948 | Helped |
| LEFA-BR vs KRAM-BR on Bridges | LEFA · Bridges | Hamming Accuracy: .840 -> .816 | Hurt |
| LEFA-ECC vs ECC on Bridges | ECC · Bridges | Hamming Accuracy: .691 -> .684 | No clear effect |
| LEFA-BR vs KRAM-BR | LEFA · Emotions | Example Accuracy: .266 -> .306 | Helped |
| LEFA-ECC vs KRAM-ECC | LEFA · Scene | Example Accuracy: .698 -> .712 | Helped |
| vary latent dimension u from 2 to 10 (ablation) | LEFA · three datasets | accuracy | No clear effect |
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