Research map / Label-efficient and robust learning / Recognition and long-tail
Active Learning with Query Generation for Cost-Effective Text Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| approximate generated queries with summary words instead of nearest real documents (ALQG vs ALQG-NN) (ablation) | ALQG · IT-vs-Learning, Healthy-vs-Auto, Culture-vs-Military, World-vs-Sports | AUC/accuracy vs annotation cost | Helped |
| use query generation (ALQG-NN) instead of uncertainty sampling over the pool (ablation) | SVM active learner · four text datasets | AUC/accuracy vs annotation cost | Helped |
| use ALQG instead of random sampling | SVM active learner · four text datasets | AUC/accuracy vs annotation cost | Helped |
Closest papers by meaning
- Exemplar Guided Active Learning (NeurIPS 2020 · similarity 0.87)
- Active Sampling for Text Classification with Subinstance Level Queries (AAAI 2022 · similarity 0.87)
- Re-Active Learning: Active Learning with Relabeling (AAAI 2016 · similarity 0.86)
- Active Classification with Few Queries under Misspecification (NeurIPS 2024 · similarity 0.85)
- Universal Rates for Active Learning (NeurIPS 2024 · similarity 0.84)
- Instance-wise Supervision-level Optimization in Active Learning (CVPR 2025 · similarity 0.84)
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