論文閱讀之 A Convex Optimization Framework for Active Learning
A Convex Optimization Framework for Active Learning Active learning is the problem of progressively selecting and annotating the most informative unlabeled samples, in order to obtain a high classif
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相關文章
- 1. 閱讀論文：Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
- 2. 論文閱讀：DRN: A Deep Reinforcement Learning Framework for News Recommendation
- 3. 論文閱讀筆記 Meta-Learning for Batch Mode Active Learning
- 4. 論文閱讀筆記《Active One-shot Learning》
- 5. 【論文筆記】Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
- 6. 論文閱讀《ActiveStereoNet:End-to-End Self-Supervised Learning for Active Stereo Systems》
- 7. 論文閱讀 Prototypical Networks for Few-shot Learning
- 8. 論文閱讀：《Wide & Deep Learning for Recommender Systems》
- 9. 《AD-VAT: AN ASYMMETRIC DUELING MECHANISM FOR LEARNING VISUAL ACTIVE TRACKING》ICLR2019論文閱讀
- 10. Convex Optimization