[解讀] MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks
儘管生成對抗網絡 (GANs) 在圖像合成任務中取得了巨大成功, 但它們在適應不同數據集方面卻異常困難, 部分原因是訓練期間的不穩定以及對超參數的敏感性. 引起這種不穩定的一個普遍認爲的原因是, 當真實和生成分佈的支撐沒有足夠的重疊時, 從鑑別器到生成器的梯度將變得無用. 本文提出了多尺度梯度生成對抗網絡(MSG-GAN), 這是一種簡單但有效的技術, 通過允許從鑑別器到生成器的梯度流向多個尺度來
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相關文章
- 1. [解讀] Coupled Generative Adversarial Networks
- 2. Binary Generative Adversarial Networks for Image Retrieval
- 3. CartoonGAN: Generative Adversarial Networks for Photo Cartoonization
- 4. Generative Adversarial Networks for Hyperspectral Image Classification
- 5. Graphical Generative Adversarial Networks
- 6. Triangle Generative Adversarial Networks
- 7. Contrastive Generative Adversarial Networks
- 8. [解讀] ClusterGAN : Latent Space Clustering in Generative Adversarial Networks
- 9. Self-Attention Generative Adversarial Networks 論文解讀
- 10. Generative Adversarial Networks overview（2）