【ReID】【skimming】Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in
補一下2017年的ICCV paper，Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro。文章首次將GAN（DCGAN）引入ReID任務中，提出了一個不需要額外蒐集data，而只依靠training data，以GAN生成無標籤的數據，且提出了label smoothi
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- 1. ICCV2017: Unlabeled Samples Generated by GAN Improve the Person Re-Identification Baseline in Vitro
- 2. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro 論文閱讀
- 3. Multi-pseudo Regularized Label for Generated Samples in Person Re-Identification（多僞正則化標籤(MpRL)、GAN）
- 4. 【ReID】【Skimming】Unity Style Transfer for Person Re-Identification
- 5. 【ReID】Strong Baseline
- 6. 【ReID】【Skimming】Unsupervised Cross-Dataset Transfer Learning for Person Re-identification
- 7. 行人重識別(ReID)開源代碼
- 8. NVIDIA新作解讀：用GAN生成高質量行人圖像，輔助行人重識別
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相關文章
- 1. ICCV2017: Unlabeled Samples Generated by GAN Improve the Person Re-Identification Baseline in Vitro
- 2. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro 論文閱讀
- 3. Multi-pseudo Regularized Label for Generated Samples in Person Re-Identification（多僞正則化標籤(MpRL)、GAN）
- 4. 【ReID】【Skimming】Unity Style Transfer for Person Re-Identification
- 5. 【ReID】Strong Baseline
- 6. 【ReID】【Skimming】Unsupervised Cross-Dataset Transfer Learning for Person Re-identification
- 7. 行人重識別(ReID)開源代碼
- 8. NVIDIA新作解讀：用GAN生成高質量行人圖像，輔助行人重識別
- 9. REID介紹（初探）-REID Baseline
- 10. 【ReID】Bag of Tricks and A Strong Baseline for Deep Person Re-identification