Learning Transferable UAV for Forest Visual Perception 的理解（一）
這篇文章本質上就是結合了兩篇文章，一篇Learning Transferable Features with Deep Adaptation Networks（有關卷積網絡與遷移學習的），通過使用多核最大均值差異成爲正則化項從而在有監督樣本和無監督樣本間實現遷移學習，達到無監督樣本分類準確率提升。 一篇A Machine Learning Approach to Visual Perception
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相關文章
- 1. Learning Transferable Architectures for Scalable Image Recognition
- 2. Learning Transferable Architectures for Scalable image recognition
- 3. AVPL: Augmented Visual Perception Learning for Person Re-identification and Beyond
- 4. 筆記——Learning Transferable Architectures for Scalable Image Recognition
- 5. Fast Underwater Image Enhancement for Improved Visual Perception
- 6. 2019 AAAI Transferable Attention for Domain Adaptation
- 7. Learning Transferable Features with Deep Adaptation Networks
- 8. CREST: Convolutional Residual Learning for Visual Tracking
- 9. Momentum Contrast for Unsupervised Visual Representation Learning
- 10. [解讀] Deep Clustering for Unsupervised Learning of Visual Features