- Q1: Deep Convolutional GAN (DCGAN)
- Papers:
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- One-sided Label Smoothing (Improved Techniques for Training GANs)
- Add Noise (Towards Principled Methods for Training Generative Adversarial Networks)
- Q2: AC-GAN, WGAN
- Papers:
- Conditional Image Synthesis With Auxiliary Classifier GANs
- Wasserstein GAN (Arjovsky et al. 2017)
HesamAsad/NeuralNetworksDeepLearning-Fall2022-CA6
Computer Assignment #6 [GANs, DCGAN, AC-GAN, Wasserstein Loss (WGAN)] - Neural Networks & Deep Learning Course - University of Tehran - Dr. Kalhor
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