Destruction and Construction Learning for Fine-grained Image Recognition
Destruction and Construction Learning for Fine-grained Image Recognition abstract 本文提出一种“破坏和构件的学习”简称为DCL的方法，来增加细粒度识别的难度，强行让分类模型获取专家的知识。提出的方法在训练期间不需要任何额外知识，在预测时除了标准分类网络外，没有计算开销。 1. Introduction 细粒度引起广泛
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相关文章
- 1. Destruction and Construction Learning for Fine-grained Image Recognition
- 2. 论文笔记20 -- Destruction and Construction Learning for Fine-grained Image Recognition
- 3. 2019CVPR细粒度论文笔记《Destruction and Construction Learning for Fine-grained Image Recognition》
- 4. 度量学习、细粒度识别相关论文阅读笔记（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