【論文閱讀】Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift

時間 2021-01-09

標籤論文閱讀遷移學習 multi source unsupervised DA 欄目系統網絡简体版

原文原文鏈接

Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift [email protected] 2020/5/12 文章目錄 1. Method abstract during training, two alternating adaptation steps: 2. Motivat

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1. Deep Cocktail Network
2. 【論文閱讀】Universal Domain Adaptation
3. [cvpr2017]Deep Hashing Network for Unsupervised Domain Adaptation
4. M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning
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6. 【論文閱讀筆記】——Batch Normalization:Accelerating Deep Network Traning by Reducing Internal Covariate Shift
7. 【論文閱讀】Domain Adaptation for Image Dehazing
8. [論文閱讀筆記@2019.6] Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
9. Unsupervised Domain Adaptation Through Self-Supervision論文閱讀筆記
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1. Deep Cocktail Network
2. 【論文閱讀】Universal Domain Adaptation
3. [cvpr2017]Deep Hashing Network for Unsupervised Domain Adaptation
4. M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning
5. Re-weighted adversarial adaptation network for unsupervised domain adaptation
6. 【論文閱讀筆記】——Batch Normalization:Accelerating Deep Network Traning by Reducing Internal Covariate Shift
7. 【論文閱讀】Domain Adaptation for Image Dehazing
8. [論文閱讀筆記@2019.6] Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
9. Unsupervised Domain Adaptation Through Self-Supervision論文閱讀筆記
10. 論文閱讀：Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision

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