Collection of generative models in Pytorch version.
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Updated
Apr 12, 2020 - Python
Collection of generative models in Pytorch version.
Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai
Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset
Simple Implementation of many GAN models with PyTorch.
Tensorflow implementation for Conditional Convolutional Adversarial Networks.
Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.
My implementation of various GAN (generative adversarial networks) architectures like vanilla GAN (Goodfellow et al.), cGAN (Mirza et al.), DCGAN (Radford et al.), etc.
In-depth tutorials on deep learning. The first one is about image colorization using GANs (Generative Adversarial Nets).
Pytorch implementation of pix2pix for various datasets.
Text to Image Synthesis using Generative Adversarial Networks
[MICCAI'21] [Tensorflow] Retinal Vessel Segmentation using a Novel Multi-scale Generative Adversarial Network
PyTorch implementation of Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN)
Conditional Sequence Generative Adversarial Network trained with policy gradient, Implementation in Tensorflow
Code implementation for paper that "ACSCS: Crowd Counting via Adversarial Cross-Scale Consistency Pursuit"; This is method of Crowd counting by conditional generation adversarial networks
Generative Adversarial Networks in TensorFlow 2.0
Implementation of paper everybody dance now for Deep learning course project
An enhanced zi2zi project with word-oriented data augmentation, feature combination, and transfer learning.
Conditioning of three-dimensional geological and pore scale generative adversarial networks
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