Learning to solve nonlinear least squares for monocular stereo閱讀筆記
1.通過神經網絡計算迭代更新來實現更好的最值逼近【19】。 1.1使用長短期記憶迴歸神經網絡（LSTM-RNN）來預測更新 。因爲雅可比矩陣結構常常表現出特定的傾向性，在將雅可比矩陣引入網絡之前對其作變換， 。通常優化計算算法中使用的是Hessian的近似，這篇文章使用LSTM-RNN網絡來實現更強大的函數逼近能力。
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相關文章
- 1. 論文閱讀筆記《Learning monocular depth estimation infusing traditional stereo knowledge》
- 2. Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
- 3. 論文閱讀筆記《End-to-End Learning of Geometry and Context for Deep Stereo Regression》
- 4. 論文閱讀《ActiveStereoNet:End-to-End Self-Supervised Learning for Active Stereo Systems》
- 5. 論文研讀：2016 DeMoN Depth and Motion Network for Learning Monocular Stereo
- 6. 論文閱讀筆記《Meta-SGD: Learning to Learn Quickly for Few-Shot Learning》
- 7. 論文閱讀筆記《Learning to Compare: Relation Network for Few-Shot Learning》
- 8. FeUdal Networks for Hierarchical Reinforcement Learning 閱讀筆記
- 9. Matching Networks for one Shot Learning 閱讀筆記
- 10. Deep Reinforcement Learning for Dialogue Generation閱讀筆記