GAN系列：論文閱讀——MSGAN(Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis)
這篇論文來自2019CVPR，用以解決conditional GAN中的mode collapse問題，改善圖像生成的多樣性。網絡 mode collapse是指生成的圖像多樣性較差，很是接近數據集中的某一種，以試圖矇騙判別器。產生緣由是：數據集中的圖像可分爲多個mode(其實就是幾大類)，有的mode中的圖像比較多，稱爲large mode，有的稱爲small mode。對應到分佈中，就是lar
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相關文章
- 1. DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis 論文解讀
- 2. Generative Adversarial Text to Image Synthesis 論文解讀
- 3. Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks
- 4. Generative Adversarial Text to Image Synthesis
- 5. Generative Adversarial Networks for Hyperspectral Image Classification
- 6. 《Generative Adversarial Text to Image Synthesis》閱讀理解
- 7. Binary Generative Adversarial Networks for Image Retrieval
- 8. GAN系列：論文閱讀——Generative Adversarial Networks & 李宏毅老師GAN課程P1+P4
- 9. 論文閱讀——Towards Adversarial Retinal Image Synthesis
- 10. DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks閱讀筆記