課程簡(jiǎn)介
GAN生成式對(duì)抗網(wǎng)絡(luò)
目標(biāo)收益
培訓(xùn)對(duì)象
對(duì)深度學(xué)習(xí)算法原理和應(yīng)用感興趣,具有一定編程(Python)和數(shù)學(xué)基礎(chǔ)(線性代數(shù)、微積分、概率論)的技術(shù)人員。
對(duì)深度學(xué)習(xí)模型,特別是生成式模型(有一定了解為佳。
課程大綱
1. GAN 入門 - Generative Models |
- Latent Factors - Generative Models |
2. GAN 原理 |
- Discrimination and Generator - Training GAN - Distances in GAN: from KL-Divergence to JS-Divergence and others - Problems with GAN - CGAN and DCGAN |
3. f-GAN 模型 - GAN 模型的同一框架 |
- Fenchel Conjugate - f-Divergence - Training: double loop vs single loop - 多種 divergence 函數(shù) |
4. Wasserstein GAN - WGAN |
- Problem with JS-Divergence - Mode Collapse - Earth-Mover Distance - WGAN - EBGAN: Energy-Based GAN |
5. InfoGAN - 可解釋表示的 GAN |
- 潛因子與表象的互信息 - 現(xiàn)有 GAN 和 Domain 之間的矛盾 - 用無(wú)監(jiān)督學(xué)習(xí)發(fā)現(xiàn)可解釋的潛因子 - 帶互信息正則項(xiàng)的 loss 函數(shù) - 實(shí)現(xiàn):用變分法進(jìn)行訓(xùn)練 - 效果 |
6. GAN for NLP |
- Improving Sequence Generation with GAN - Conditional Sequence Generation - Unsupervised Conditional Sequence Generation |
7. GAN for CV |
- GAN + Autoencoder: Photo Editing - Image Super Resolution - Image Completion |
1. GAN 入門 - Generative Models - Latent Factors - Generative Models |
2. GAN 原理 - Discrimination and Generator - Training GAN - Distances in GAN: from KL-Divergence to JS-Divergence and others - Problems with GAN - CGAN and DCGAN |
3. f-GAN 模型 - GAN 模型的同一框架 - Fenchel Conjugate - f-Divergence - Training: double loop vs single loop - 多種 divergence 函數(shù) |
4. Wasserstein GAN - WGAN - Problem with JS-Divergence - Mode Collapse - Earth-Mover Distance - WGAN - EBGAN: Energy-Based GAN |
5. InfoGAN - 可解釋表示的 GAN - 潛因子與表象的互信息 - 現(xiàn)有 GAN 和 Domain 之間的矛盾 - 用無(wú)監(jiān)督學(xué)習(xí)發(fā)現(xiàn)可解釋的潛因子 - 帶互信息正則項(xiàng)的 loss 函數(shù) - 實(shí)現(xiàn):用變分法進(jìn)行訓(xùn)練 - 效果 |
6. GAN for NLP - Improving Sequence Generation with GAN - Conditional Sequence Generation - Unsupervised Conditional Sequence Generation |
7. GAN for CV - GAN + Autoencoder: Photo Editing - Image Super Resolution - Image Completion |