Deep Semantic Hashing with Generative Adversarial Networks 論文review（翻譯）
Deep Semantic Hashing with Generative Adversarial Networks 創新點： a) 通過半監督網絡生成合成數據解決了監督哈希數據集的問題 b) 提出結合對抗網絡的哈希結構，增強了哈希模型的泛化性，生成哈希碼，不僅保存了圖像之間的相似性，還保留了圖像的語意（分類信息） background a) 現有大規模圖像檢索方法：hashing techniq
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相關文章
- 1. 論文譯文《ANOMALY DETECTION WITH GENERATIVE ADVERSARIAL NETWORKS》
- 2. UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS
- 3. ESRGAN - Enhanced Super-Resolution Generative Adversarial Networks論文翻譯——中英文對照
- 4. ESRGAN - Enhanced Super-Resolution Generative Adversarial Networks論文翻譯——中文版
- 5. 論文翻譯：Speech Super Resolution Generative Adversarial Network
- 6. DCGAN：Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- 7. 論文閱讀——《Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks》
- 8. DCGAN:Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks論文
- 9. 【翻譯】Image-to-Image Translation with Conditional Adversarial Networks
- 10. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS