Deep Learning With PythonExample projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.
Stars: ✭ 134 (-45.75%)
Pytorch RlThis repository contains model-free deep reinforcement learning algorithms implemented in Pytorch
Stars: ✭ 394 (+59.51%)
Generative ModelsAnnotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Stars: ✭ 438 (+77.33%)
Vae TensorflowA Tensorflow implementation of a Variational Autoencoder for the deep learning course at the University of Southern California (USC).
Stars: ✭ 117 (-52.63%)
CycleganSoftware that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
Stars: ✭ 10,933 (+4326.32%)
GanimationGANimation: Anatomically-aware Facial Animation from a Single Image (ECCV'18 Oral) [PyTorch]
Stars: ✭ 1,730 (+600.4%)
Gan steerabilityOn the "steerability" of generative adversarial networks
Stars: ✭ 225 (-8.91%)
Tensorflow Mnist CvaeTensorflow implementation of conditional variational auto-encoder for MNIST
Stars: ✭ 139 (-43.72%)
The Gan WorldEverything about Generative Adversarial Networks
Stars: ✭ 243 (-1.62%)
The Gan ZooA list of all named GANs!
Stars: ✭ 11,454 (+4537.25%)
Mlds2018springMachine Learning and having it Deep and Structured (MLDS) in 2018 spring
Stars: ✭ 124 (-49.8%)
Capsule GanCode for my Master thesis on "Capsule Architecture as a Discriminator in Generative Adversarial Networks".
Stars: ✭ 120 (-51.42%)
UnetganOfficial Implementation of the paper "A U-Net Based Discriminator for Generative Adversarial Networks" (CVPR 2020)
Stars: ✭ 139 (-43.72%)
GifGIF is a photorealistic generative face model with explicit 3D geometric and photometric control.
Stars: ✭ 233 (-5.67%)
Tsit[ECCV 2020 Spotlight] A Simple and Versatile Framework for Image-to-Image Translation
Stars: ✭ 141 (-42.91%)
GandissectPytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/
Stars: ✭ 1,700 (+588.26%)
FrontalizationPytorch deep learning face frontalization model
Stars: ✭ 160 (-35.22%)
Vae Cvae MnistVariational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch
Stars: ✭ 229 (-7.29%)
Anime Face Gan KerasA DCGAN to generate anime faces using custom mined dataset
Stars: ✭ 161 (-34.82%)
GannotationGANnotation (PyTorch): Landmark-guided face to face synthesis using GANs (And a triple consistency loss!)
Stars: ✭ 167 (-32.39%)
AdganThe Implementation of paper "Controllable Person Image Synthesis with Attribute-Decomposed GAN"
Stars: ✭ 239 (-3.24%)
Gan SandboxVanilla GAN implemented on top of keras/tensorflow enabling rapid experimentation & research. Branches correspond to implementations of stable GAN variations (i.e. ACGan, InfoGAN) and other promising variations of GANs like conditional and Wasserstein.
Stars: ✭ 210 (-14.98%)
Hccg CycleganHandwritten Chinese Characters Generation
Stars: ✭ 115 (-53.44%)
O GanO-GAN: Extremely Concise Approach for Auto-Encoding Generative Adversarial Networks
Stars: ✭ 117 (-52.63%)
GpndGenerative Probabilistic Novelty Detection with Adversarial Autoencoders
Stars: ✭ 112 (-54.66%)
Tensorflow Mnist Cgan CdcganTensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.
Stars: ✭ 122 (-50.61%)
Rectorchrectorch is a pytorch-based framework for state-of-the-art top-N recommendation
Stars: ✭ 121 (-51.01%)
ExermoteUsing Machine Learning to predict the type of exercise from movement data
Stars: ✭ 108 (-56.28%)
Triple GanSee Triple-GAN-V2 in PyTorch: https://github.com/taufikxu/Triple-GAN
Stars: ✭ 203 (-17.81%)
Generative adversarial networks 101Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.
Stars: ✭ 138 (-44.13%)
IseebetteriSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press
Stars: ✭ 202 (-18.22%)
Nice Gan PytorchOfficial PyTorch implementation of NICE-GAN: Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation
Stars: ✭ 140 (-43.32%)
P2palaPage to PAGE Layout Analysis Tool
Stars: ✭ 147 (-40.49%)
MojitalkCode for "MojiTalk: Generating Emotional Responses at Scale" https://arxiv.org/abs/1711.04090
Stars: ✭ 107 (-56.68%)
Cada Vae PytorchOfficial implementation of the paper "Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders" (CVPR 2019)
Stars: ✭ 198 (-19.84%)
Anogan KerasUnsupervised anomaly detection with generative model, keras implementation
Stars: ✭ 157 (-36.44%)
S Vae TfTensorflow implementation of Hyperspherical Variational Auto-Encoders
Stars: ✭ 198 (-19.84%)
ShapeganGenerative Adversarial Networks and Autoencoders for 3D Shapes
Stars: ✭ 151 (-38.87%)
Anogan TfUnofficial Tensorflow Implementation of AnoGAN (Anomaly GAN)
Stars: ✭ 218 (-11.74%)
Tensorflow Mnist Gan DcganTensorflow implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Netwokrs for MNIST dataset.
Stars: ✭ 163 (-34.01%)
Stylegan.pytorchA PyTorch implementation for StyleGAN with full features.
Stars: ✭ 150 (-39.27%)
Gan2shapeCode for GAN2Shape (ICLR2021 oral)
Stars: ✭ 183 (-25.91%)
FreezedFreeze the Discriminator: a Simple Baseline for Fine-Tuning GANs (CVPRW 2020)
Stars: ✭ 195 (-21.05%)
RanksrganICCV 2019 (oral) RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution. PyTorch implementation
Stars: ✭ 213 (-13.77%)
Pytorch VaeA CNN Variational Autoencoder (CNN-VAE) implemented in PyTorch
Stars: ✭ 181 (-26.72%)
Adversarial video summaryUnofficial PyTorch Implementation of SUM-GAN from "Unsupervised Video Summarization with Adversarial LSTM Networks" (CVPR 2017)
Stars: ✭ 187 (-24.29%)
SmrtHandle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.
Stars: ✭ 102 (-58.7%)