Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Sit
Abstract 論文作者提出： In some label insufficient situations, the performance degrades on the decision boundary with high data density. Acommon solution is to directly minimize the Shannon Entropy, but the
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相關文章
- 1. Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Sit
- 2. CVPR 2020(Oral) | 中科院等提出BNM：標籤不充分下的判別性與多樣性約束方法
- 3. Evolutionary approaches towards AI: past, present, and future
- 4. Clustering：Gaussian Mixture Model and Expectation Maximization
- 5. CVPR/AAAI/ECCV頂會論文/代碼
- 6. CF677C Vanya and Label
- 7. tf.keras入門(4) Explore over-fitting and under-fitting
- 8. Covariate shift and batch normalization
- 9. DIM（Learning deep representations by mutual information estimation and maximization）
- 10. Batch Training