283 Open source projects that are alternatives of or similar to cDCGAN

Simple Implementation of many GAN models with PyTorch.

Tensorflow implementation of different GANs and their comparisions

A Pytorch implementation of "Deep Convolutional Generative Adversarial Networks"

Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets

Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.

Invert the color of MNIST images with PyTorch

Study Friendly Implementation of DCGAN in Tensorflow

Periodic Spatial Generative Adversarial Networks

No description or website provided.

Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"

Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai

GAN in Tensorflow to be run via Linux command line

Tensorflow implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Netwokrs for MNIST dataset.

Collection of generative models in Pytorch version.

Generative Adversarial Networks in TensorFlow 2.0

DCGAN Implementation using PyTorch in both C++ and Python

[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.

Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset

Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking

Collection of generative models in Tensorflow

Experiments for understanding disentanglement in VAE latent representations

This project uses a conditional generative adversarial network (cGAN) named Pix2Pix for the Image to image translation task.

Tensorflow-example：使用MNIST训练模型，并识别手写数字图片

手写数字识别。利用Tensorflow和MNIST实现的一个简单的手写数字识别程序。

Predict handwritten digits with CoreML

Playing with MNIST. Machine Learning. Generative Models.

Recognize your own handwritten digits with Tensorflow, embedded in a PyQT5 GUI. The Neural Network was trained on MNIST.

In-Browser Digit recognition with Tensorflow.js and React using Mnist dataset

Pytorch reimplementation for "Gradient Surgery for Multi-Task Learning"

CNN classification model built in Keras used for Digit Recognizer task on Kaggle (https://www.kaggle.com/c/digit-recognizer)

Tensorflow implementation of adversarial auto-encoder for MNIST

Conditional Sequence Generative Adversarial Network trained with policy gradient, Implementation in Tensorflow

A curated list of dedicated resources and applications

One-Shot Learning with Triplet CNNs in Pytorch

Implementation of Conditional Generative Adversarial Networks in PyTorch

A small convolution neural network deep learning framework implemented in c++.

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).

CNN digit recognizer implemented in Keras Notebook, Kaggle/MNIST (0.995).

MXNet Implementation of DCGAN, Conditional GAN, pix2pix

Minimalist implementation of VQ-VAE in Pytorch

A scratch implementation of Convolutional Neural Network in Python using only numpy and validated over CIFAR-10 & MNIST Dataset

PyTorch implementation of GANs (Generative Adversarial Networks). DCGAN, Pix2Pix, CycleGAN, SRGAN

Çeşitli kütüphaneler kullanılarak Türkçe kod açıklamalarıyla TEMEL SEVİYEDE pratik derin öğrenme uygulamaları.

This project is a semi-supervised approach to detect emotions on faces in-the-wild using GAN

Lingvo

dcgan combined with vae in pytorch!

A PyTorch implementation of alpha-GAN

MNIST with TensorFlow Lite on Android

Tensorflow 2.0 implementation of Adversarial Autoencoders

Generative Adversarial Network for MNIST with tensorflow

MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.

Image to Image Translation using Conditional GANs (Pix2Pix) implemented using Tensorflow 2.0

PyTorch Impl. of Prediction Optimizer (to stabilize GAN training)

Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10

Implementation of paper everybody dance now for Deep learning course project

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Experiments on cluttered mnist dataset with Tensorflow.