论文阅读 Jointly Optimize Data Augmentation and Network Training
平常的过，再过几分钟就25岁了，不知道怎么捕捉这个时刻，越来越喜欢孤独的感觉，常哭，常生气，希望未来的人会出现吧，真的要长大了 –槛外人– Abstract 随机的数据扩增对于网络训练很重要，以前的方法是数据扩增和网络训练是分开的，本文设计一种自动的扩增网络来产生hard的样本扩增，augmentation网络探索了target(hourglass)网络的弱点，target网络从hard样本中去实
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相关文章
- 1. Unsupervised Data Augmentation For Consistency Training 论文阅读
- 2. 论文阅读：AutoAugment: Learning Augmentation Strategies from Data
- 3. 论文阅读《SUPERPOSITION AS DATA AUGMENTATION USING LSTM AND HMM IN SMALL TRAINING SETS》
- 4. 论文阅读之《Random Erasing Data Augmentation》
- 5. 【论文阅读笔记】Random Erasing Data Augmentation
- 6. 论文阅读：Learning Data Augmentation Strategies for Object Detection
- 7. 论文阅读：EfficientDet: Scalable and Efficient Object Detection
- 8. 论文阅读：Volumetric and Multi-View CNNs for Object Classification on 3D Data
- 9. 《Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements》阅读笔记
- 10. Neural machine translation by jointly learning to align and translate论文阅读笔记