論文閱讀 Jointly Optimize Data Augmentation and Network Training
平常的過，再過幾分鐘就25歲了，不知道怎麼捕捉這個時刻，越來越喜歡孤獨的感覺，常哭，常生氣，希望未來的人會出現吧，真的要長大了 –檻外人– Abstract 隨機的數據擴增對於網絡訓練很重要，以前的方法是數據擴增和網絡訓練是分開的，本文設計一種自動的擴增網絡來產生hard的樣本擴增，augmentation網絡探索了target(hourglass)網絡的弱點，target網絡從hard樣本中去實
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相關文章
- 1. Unsupervised Data Augmentation For Consistency Training 論文閱讀
- 2. 論文閱讀：AutoAugment: Learning Augmentation Strategies from Data
- 3. 論文閱讀《SUPERPOSITION AS DATA AUGMENTATION USING LSTM AND HMM IN SMALL TRAINING SETS》
- 4. 論文閱讀之《Random Erasing Data Augmentation》
- 5. 【論文閱讀筆記】Random Erasing Data Augmentation
- 6. 論文閱讀：Learning Data Augmentation Strategies for Object Detection
- 7. 論文閱讀：EfficientDet: Scalable and Efficient Object Detection
- 8. 論文閱讀：Volumetric and Multi-View CNNs for Object Classification on 3D Data
- 9. 《Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements》閱讀筆記
- 10. Neural machine translation by jointly learning to align and translate論文閱讀筆記