MMD-GANImproving MMD-GAN training with repulsive loss function
Stars: ✭ 82 (-88.18%)
CPCE-3DLow-dose CT via Transfer Learning from a 2D Trained Network, In IEEE TMI 2018
Stars: ✭ 40 (-94.24%)
pytorch-GANMy pytorch implementation for GAN
Stars: ✭ 12 (-98.27%)
text2image-benchmarkPerformance comparison of existing GAN based Text To Image algorithms. (GAN-CLS, StackGAN, TAC-GAN)
Stars: ✭ 25 (-96.4%)
TF2-GAN🐳 GAN implemented as Tensorflow 2.X
Stars: ✭ 61 (-91.21%)
SDGymBenchmarking synthetic data generation methods.
Stars: ✭ 177 (-74.5%)
triplet-loss-pytorchHighly efficient PyTorch version of the Semi-hard Triplet loss ⚡️
Stars: ✭ 79 (-88.62%)
SSVEP-Neural-Generative-ModelsCode to accompany our International Joint Conference on Neural Networks (IJCNN) paper entitled - Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification
Stars: ✭ 37 (-94.67%)
text2imageNetGenerate image from text with Generative Adversarial Network
Stars: ✭ 26 (-96.25%)
ganbertEnhancing the BERT training with Semi-supervised Generative Adversarial Networks
Stars: ✭ 205 (-70.46%)
Self-Supervised-GANsTensorflow Implementation for paper "self-supervised generative adversarial networks"
Stars: ✭ 34 (-95.1%)
hyperstyleOfficial Implementation for "HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing" (CVPR 2022) https://arxiv.org/abs/2111.15666
Stars: ✭ 874 (+25.94%)
SphereFace🍑 TensorFlow Code for CVPR 2017 paper "SphereFace: Deep Hypersphere Embedding for Face Recognition"
Stars: ✭ 110 (-84.15%)
adversarial-networksMaterial de la charla "The bad guys in AI - atacando sistemas de machine learning"
Stars: ✭ 15 (-97.84%)
bmuseganCode for “Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation”
Stars: ✭ 58 (-91.64%)
simpleganTensorflow-based framework to ease training of generative models
Stars: ✭ 19 (-97.26%)
MAD-GAN-MLCAMPRepository for MAD-GAN Paper done in ML CAMP Jeju
Stars: ✭ 17 (-97.55%)
AGD[ICML2020] "AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks" by Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan Lin, Zhangyang Wang
Stars: ✭ 98 (-85.88%)
Deep-Learning-PytorchA repo containing code covering various aspects of deep learning on Pytorch. Great for beginners and intermediate in the field
Stars: ✭ 59 (-91.5%)
ResNet-50-CBAM-PyTorchImplementation of Resnet-50 with and without CBAM in PyTorch v1.8. Implementation tested on Intel Image Classification dataset from https://www.kaggle.com/puneet6060/intel-image-classification.
Stars: ✭ 31 (-95.53%)
DCGAN-CIFAR10A implementation of DCGAN (Deep Convolutional Generative Adversarial Networks) for CIFAR10 image
Stars: ✭ 18 (-97.41%)
DeepEchoSynthetic Data Generation for mixed-type, multivariate time series.
Stars: ✭ 44 (-93.66%)
gan deeplearning4jAutomatic feature engineering using Generative Adversarial Networks using Deeplearning4j and Apache Spark.
Stars: ✭ 19 (-97.26%)
gans-2.0Generative Adversarial Networks in TensorFlow 2.0
Stars: ✭ 76 (-89.05%)
ST-CGANDataset and Code for our CVPR'18 paper ST-CGAN: "Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal"
Stars: ✭ 64 (-90.78%)
gzsl-odOut-of-Distribution Detection for Generalized Zero-Shot Action Recognition
Stars: ✭ 47 (-93.23%)
path planning GANPath Planning using Generative Adversarial Network (GAN)
Stars: ✭ 36 (-94.81%)
consistencyImplementation of models in our EMNLP 2019 paper: A Logic-Driven Framework for Consistency of Neural Models
Stars: ✭ 26 (-96.25%)
Advanced Models여러가지 유명한 신경망 모델들을 제공합니다. (DCGAN, VAE, Resnet 등등)
Stars: ✭ 48 (-93.08%)
PESROfficial code (Pytorch) for paper Perception-Enhanced Single Image Super-Resolution via Relativistic Generative Networks
Stars: ✭ 28 (-95.97%)
Awesome-GAN-Resources🤖A list of resources to help anyone getting started with GANs 🤖
Stars: ✭ 90 (-87.03%)
CIPS-3D3D-aware GANs based on NeRF (arXiv).
Stars: ✭ 562 (-19.02%)
BPPNet-Back-Projected-Pyramid-NetworkThis is the official GitHub repository for ECCV 2020 Workshop paper "Single image dehazing for a variety of haze scenarios using back projected pyramid network"
Stars: ✭ 35 (-94.96%)
Deep-FakesNo description or website provided.
Stars: ✭ 88 (-87.32%)
Easter-Bootcamp-2018Designed to take you from zero experience to GANs within a week.
Stars: ✭ 24 (-96.54%)
DiscoGAN-TFTensorflow Implementation of DiscoGAN
Stars: ✭ 57 (-91.79%)
timegan-pytorchThis repository is a non-official implementation of TimeGAN (Yoon et al., NIPS2019) using PyTorch.
Stars: ✭ 46 (-93.37%)
WGAN-GP-tensorflowTensorflow Implementation of Paper "Improved Training of Wasserstein GANs"
Stars: ✭ 23 (-96.69%)
GIouloss CIouloss caffeCaffe version Generalized & Distance & Complete Iou loss Implementation for Faster RCNN/FPN bbox regression
Stars: ✭ 42 (-93.95%)
DCGAN-PytorchA Pytorch implementation of "Deep Convolutional Generative Adversarial Networks"
Stars: ✭ 23 (-96.69%)
SMILESMILE: Semantically-guided Multi-attribute Image and Layout Editing, ICCV Workshops 2021.
Stars: ✭ 28 (-95.97%)
binaryganCode for "Training Generative Adversarial Networks with Binary Neurons by End-to-end Backpropagation"
Stars: ✭ 25 (-96.4%)
DeepSIMOfficial PyTorch implementation of the paper: "DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample" (ICCV 2021 Oral)
Stars: ✭ 389 (-43.95%)
GAN-LTH[ICLR 2021] "GANs Can Play Lottery Too" by Xuxi Chen, Zhenyu Zhang, Yongduo Sui, Tianlong Chen
Stars: ✭ 24 (-96.54%)
GraphCNN-GANGraph-convolutional GAN for point cloud generation. Code from ICLR 2019 paper Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
Stars: ✭ 50 (-92.8%)
Paper-NotesPaper notes in deep learning/machine learning and computer vision
Stars: ✭ 37 (-94.67%)
Sketch2Color-anime-translationGiven a simple anime line-art sketch the model outputs a decent colored anime image using Conditional-Generative Adversarial Networks (C-GANs) concept.
Stars: ✭ 90 (-87.03%)
videoDCGANImplementation of a GAN that generates video using LSTM and ConvNet in Tensorflow
Stars: ✭ 14 (-97.98%)
gan-error-avoidanceLearning to Avoid Errors in GANs by Input Space Manipulation (Code for paper)
Stars: ✭ 23 (-96.69%)
GeDMLGeneralized Deep Metric Learning.
Stars: ✭ 30 (-95.68%)