Research map / Label-efficient and robust learning / Recognition and long-tail
Learning Deep Latent Space for Multi-Label Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use C2AE instead of BP-MLL | DNN-based multi-label classification · NUS-WIDE | C-P: 44.5 -> 55.8 | Helped |
| Use C2AE instead of BP-MLL | DNN-based multi-label classification · NUS-WIDE | C-R: 39.8 -> 45.3 | Helped |
| Use C2AE instead of BP-MLL | DNN-based multi-label classification · NUS-WIDE | C-F1: 38.3 -> 48.6 | Helped |
| Use C2AE instead of BP-MLL | DNN-based multi-label classification · NUS-WIDE | O-P: 57.3 -> 66.2 | Helped |
| Use C2AE instead of BP-MLL | DNN-based multi-label classification · NUS-WIDE | O-R: 68.9 -> 69.1 | Helped |
| Use C2AE instead of BP-MLL | DNN-based multi-label classification · NUS-WIDE | O-F1: 62.5 -> 67.6 | Helped |
Closest papers by meaning
- Modeling Label Space Interactions in Multi-label Classification using Box Embeddings (ICLR 2022 · similarity 0.89)
- Two-Stage Label Embedding via Neural Factorization Machine for Multi-Label Classification (AAAI 2019 · similarity 0.89)
- Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning (CVPR 2025 · similarity 0.85)
- Addressing Multi-Label Learning with Partial Labels: From Sample Selection to Label Selection (AAAI 2025 · similarity 0.85)
- Incomplete Multi-View Multi-label Learning via Disentangled Representation and Label Semantic Embedding (CVPR 2025 · similarity 0.85)
- Neural Collapse in Multi-label Learning with Pick-all-label Loss (ICML 2024 · similarity 0.85)
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