This repository is created for learning GANs and its applications based on the content provided by deeplearning.ai on Coursera
(1) Basic GANs
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Further Readings & References(from coursera) :
- Hyperspherical Variational Auto-Encoders (Davidson, Falorsi, De Cao, Kipf, and Tomczak, 2018): https://www.researchgate.net/figure/Latent-space-visualization-of-the-10-MNIST-digits-in-2-dimensions-of-both-N-VAE-left_fig2_324182043
- Analyzing and Improving the Image Quality of StyleGAN (Karras et al., 2020): https://arxiv.org/abs/1912.04958
- Semantic Image Synthesis with Spatially-Adaptive Normalization (Park, Liu, Wang, and Zhu, 2019): https://arxiv.org/abs/1903.07291
- Few-shot Adversarial Learning of Realistic Neural Talking Head Models (Zakharov, Shysheya, Burkov, and Lempitsky, 2019): https://arxiv.org/abs/1905.08233 Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (Wu, Zhang, Xue, Freeman, and Tenenbaum, 2017): https://arxiv.org/abs/1610.07584
- These Cats Do Not Exist (Glover and Mott, 2019): http://thesecatsdonotexist.com/
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Notebooks:
- Large Scale GAN Training for High Fidelity Natural Image Synthesis (Brock, Donahue, and Simonyan, 2019): https://arxiv.org/abs/1809.11096
- PyTorch Documentation: https://pytorch.org/docs/stable/index.html#pytorch-documentation
- MNIST Database: http://yann.lecun.com/exdb/mnist/
(2) Better Generative Adversarial Networks
- Evaluations of GANs
- GANs Disadvantages and bias
- StyleGANs and their advancements
(3) Applications of Generative Adversarial Networks (GANs)
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Learn different applications of GANs, understand the pros/cons of using them for data augmentation, and see how they can improve downstream AI models! Explore different applications of GANs and examine their specific applications in data augmentation, privacy, and anonymity.
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Improve your downstream AI models with GAN-generated data.