Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning
相關工作 reflective loss層來檢測網絡CNN網絡性能優劣 新網絡來代替WTA 1.新網絡結構， 多層多尺度控制網絡的接受域 2.通過hybrid loss來訓練網絡獲得更好的網絡結構性能 3.通過CNN得到視差圖而不是先前的WTA， 4.通過reflective來測量CNN輸出準確性 5.如何利用confidence score更好的檢測異常點並且進行修正 6.性能和速度有了很大的進
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相關文章
- 1. Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning
- 2. Self-Supervised Learning for Stereo Matching with Self-Improving Ability
- 3. Unsupervised Learning of Stereo Matching
- 4. Confidence Inference for Focused Learning in Stereo Matching 論文筆記
- 5. FixMatch Simplifying Semi-Supervised Learning with Consistency and Confidence
- 6. Highway Networks VS Deep Residual Learning
- 7. Highway Networks
- 8. Pyramid Stereo Matching Network
- 9. 《Efficient Deep Learning for Stereo Matching》學習筆記
- 10. Universally Slimmable Networks and Improved Training Techniques_ICCV_2019