(22)[NIPS16] PixelCNN：Conditional Image Generation with PixelCNN Decoders
計劃完成深度學習入門的126篇論文第二十一篇，DeepMind的Alex Graves等領導研究一種新的生成Image模型。 ABSTRACT&INTRODUCTION 摘要 本工做探索了基於PixelCNN結構的條件圖像生成新圖像密度模型。該模型能夠以任何向量爲條件，包括描述性標籤或標記，或由其餘網絡建立的潛在嵌入。當以ImageNet數據庫中的類標籤爲條件時，該模型可以生成表明不一樣動物、對象
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相關文章
- 1. 《Conditional Image Generation with PixelCNN Decoders》之Pixcel CNN---gated Pixcel CNN 閱讀筆記
- 2. 如何比較PixelCNN與DCGAN兩種Image generation方法？
- 3. Pose Guided Person Image Generation
- 4. 論文閱讀：Neural Image Caption Generation with Visual Attention
- 5. AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
- 6. [Paper Reading] Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- 7. 《Show, Attend and Tell: Neural Image Caption Generation with Visual Attention》閱讀筆記
- 8. attention(show, attention and tell: neural image caption generation with visual attention)
- 9. Automatic Trimap Generation for Image Matting
- 10. CVAE-GAN：Fine-Grained Image Generation through Asymmetric Training