Destruction and Construction Learning for Fine-grained Image Recognition
Destruction and Construction Learning for Fine-grained Image Recognition abstract 本文提出一種「破壞和構件的學習」簡稱爲DCL的方法，來增加細粒度識別的難度，強行讓分類模型獲取專家的知識。提出的方法在訓練期間不需要任何額外知識，在預測時除了標準分類網絡外，沒有計算開銷。 1. Introduction 細粒度引起廣泛
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相關文章
- 1. Destruction and Construction Learning for Fine-grained Image Recognition
- 2. 論文筆記20 -- Destruction and Construction Learning for Fine-grained Image Recognition
- 3. 2019CVPR細粒度論文筆記《Destruction and Construction Learning for Fine-grained Image Recognition》
- 4. 度量學習、細粒度識別相關論文閱讀筆記（三）—— Destruction and Construction Learning for Fine-grained Image Recognition
- 5. Deep Residual Learning for Image Recognition
- 6. ResNet--Deep Residual Learning for Image Recognition
- 7. Learning Transferable Architectures for Scalable image recognition
- 8. Learning Transferable Architectures for Scalable Image Recognition
- 9. Deep Residual Learning for Image Recognition （zh）
- 10. [ResNet]Deep Residual Learning for Image Recognition