Assisted Excitation of Activations:A Learning Technique to Improve Object Detectors論文解讀
Assisted Excitation of Activations:A Learning Technique to Improve Object Detectors 這是cvpr2019上的一篇文章，以yolo爲例，沒有修改網絡結構，也沒有增加額外的計算負擔。 （1）目的：解決yolo定位不準確和樣本不均勻的問題。 （2）改進點：在訓練階段加入Assisted Excitation（AE）模塊，
相關文章
- 1. Assisted Excitation of Activations: A Learning Technique to Improve Object Detect
- 2. Incremental Learning of Object Detectors without Catastrophic Forgetting 論文閱讀
- 3. Incremental Learning of Object Detectors without Catastrophic Forgetting詳解
- 4. DSOD： Learning Deeply Supervised Object Detectors from Scratch 論文解讀
- 5. 論文閱讀：Learning to Segment Object Candidates（DeepMask）
- 6. How To Improve Deep Learning Performance
- 7. 對抗樣本(論文解讀八):Towards More Robust Adversarial Attack Against Real World Object Detectors
- 8. 論文：Speed/accuracy trade-offs for object detectors
- 9. DSOD: Learning Deeply Supervised Object Detectors from Scratch 論文筆記
- 10. 對抗樣本(論文解讀一): DPATCH: An Adversarial Patch Attack on Object Detectors
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相關文章
- 1. Assisted Excitation of Activations: A Learning Technique to Improve Object Detect
- 2. Incremental Learning of Object Detectors without Catastrophic Forgetting 論文閱讀
- 3. Incremental Learning of Object Detectors without Catastrophic Forgetting詳解
- 4. DSOD： Learning Deeply Supervised Object Detectors from Scratch 論文解讀
- 5. 論文閱讀：Learning to Segment Object Candidates（DeepMask）
- 6. How To Improve Deep Learning Performance
- 7. 對抗樣本(論文解讀八):Towards More Robust Adversarial Attack Against Real World Object Detectors
- 8. 論文：Speed/accuracy trade-offs for object detectors
- 9. DSOD: Learning Deeply Supervised Object Detectors from Scratch 論文筆記
- 10. 對抗樣本(論文解讀一): DPATCH: An Adversarial Patch Attack on Object Detectors