GAN
Generative Adverserial Networks are an approach to generative data modelling using Deep learning methods.
I have currently implemented :
- DCGAN on CIFAR dataset.
Implementation of Gans
Overview
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StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.
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[CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing
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Code for the paper "Training GANs with Stronger Augmentations via Contrastive Discriminator" (ICLR 2021)
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EigenGAN Tensorflow, EigenGAN: Layer-Wise Eigen-Learning for GANs
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This is the codebase for Diffusion Models Beat GANS on Image Synthesis.
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PyTorch inference for "Progressive Growing of GANs" with CelebA snapshot
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Codebase for Diffusion Models Beat GANS on Image Synthesis.
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This is the PyTorch implementation of GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation
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[CVPR 2021] Pytorch implementation of Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs
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