2020CVPR去霧-Domain Adaptation for Image Dehazing Yuanjie
這篇文章針對人爲構造的訓練數據集與真實霧霾景象存在區別而導致訓練好的模型在實際應用中效果不好的問題進行了探究 整體思想是：已知兩個數據集，合成數據集（含label）S和真實數據集R，分別通過S2R網絡和R2S網絡生成另外兩個數據集S->R和R->S，共四個數據集。S和R->S兩個數據集共享生成網絡參數來訓練去霧，R和S->R兩個數據集共享另一個生成網絡（與前一個網絡結構相同）參數來訓練去霧。 整個
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相關文章
- 1. 論文筆記：Domain Adaptation for Image Dehazing
- 2. 【論文閱讀】Domain Adaptation for Image Dehazing
- 3. 2020CVPR去霧-Multi-Scale Boosted Dehazing Network with Dense Feature Fusion
- 4. [去霧論文]--AAAI2020-FFA-Net：Feature Fusion Attention Network for Single Image Dehazing
- 5. Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image
- 6. Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing
- 7. 論文筆記：去霧、超分和 Makeup（Cycle GAN 相關 4 篇）
- 8. 2019 AAAI Transferable Attention for Domain Adaptation
- 9. Image-Image Domain Adaptation with Preserved Self-Similarity andDomain-Dissimilarity for Person Re-i
- 10. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation