IganInteractive Image Generation via Generative Adversarial Networks
Stars: ✭ 3,845 (+111.96%)
Anycost Gan[CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing
Stars: ✭ 367 (-79.77%)
Co FusionCo-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects
Stars: ✭ 400 (-77.95%)
Pytorch Mnist Celeba Gan DcganPytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets
Stars: ✭ 363 (-79.99%)
SimganImplementation of Apple's Learning from Simulated and Unsupervised Images through Adversarial Training
Stars: ✭ 406 (-77.62%)
Simgan CaptchaSolve captcha without manually labeling a training set
Stars: ✭ 405 (-77.67%)
SdvSynthetic Data Generation for tabular, relational and time series data.
Stars: ✭ 360 (-80.15%)
Open3dOpen3D: A Modern Library for 3D Data Processing
Stars: ✭ 5,860 (+223.04%)
Generative ModelsAnnotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Stars: ✭ 438 (-75.85%)
Alfredalfred-py: A deep learning utility library for **human**, more detail about the usage of lib to: https://zhuanlan.zhihu.com/p/341446046
Stars: ✭ 460 (-74.64%)
Generative CompressionTensorFlow Implementation of Generative Adversarial Networks for Extreme Learned Image Compression
Stars: ✭ 428 (-76.41%)
T2fT2F: text to face generation using Deep Learning
Stars: ✭ 494 (-72.77%)
Textgan PytorchTextGAN is a PyTorch framework for Generative Adversarial Networks (GANs) based text generation models.
Stars: ✭ 479 (-73.59%)
T81 558 deep learningWashington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks
Stars: ✭ 4,152 (+128.89%)
MedicalzoopytorchA pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation
Stars: ✭ 546 (-69.9%)
Deeplearning深度学习入门教程, 优秀文章, Deep Learning Tutorial
Stars: ✭ 6,783 (+273.93%)
Cartoongan TensorflowGenerate your own cartoon-style images with CartoonGAN (CVPR 2018), powered by TensorFlow 2.0 Alpha.
Stars: ✭ 587 (-67.64%)
AlbumentationsFast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
Stars: ✭ 9,353 (+415.6%)
Pggan Pytorch🔥🔥 PyTorch implementation of "Progressive growing of GANs (PGGAN)" 🔥🔥
Stars: ✭ 653 (-64%)
HidtOfficial repository for the paper "High-Resolution Daytime Translation Without Domain Labels" (CVPR2020, Oral)
Stars: ✭ 513 (-71.72%)
Gans In ActionCompanion repository to GANs in Action: Deep learning with Generative Adversarial Networks
Stars: ✭ 748 (-58.77%)
Context Encoder[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs
Stars: ✭ 731 (-59.7%)
InstaganInstaGAN: Instance-aware Image Translation (ICLR 2019)
Stars: ✭ 761 (-58.05%)
TorchioMedical image preprocessing and augmentation toolkit for deep learning
Stars: ✭ 708 (-60.97%)
Dcgan PytorchPyTorch Implementation of DCGAN trained on the CelebA dataset.
Stars: ✭ 32 (-98.24%)
Contrastive Unpaired TranslationContrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
Stars: ✭ 822 (-54.69%)
Rectlabel SupportRectLabel - An image annotation tool to label images for bounding box object detection and segmentation.
Stars: ✭ 338 (-81.37%)
Segment OpenSegment Source Distribution
Stars: ✭ 34 (-98.13%)
MotsfusionMOTSFusion: Track to Reconstruct and Reconstruct to Track
Stars: ✭ 118 (-93.5%)
ExermoteUsing Machine Learning to predict the type of exercise from movement data
Stars: ✭ 108 (-94.05%)
GandlfGenerative Adversarial Networks in Keras
Stars: ✭ 46 (-97.46%)
Jsi GanOfficial repository of JSI-GAN (Accepted at AAAI 2020).
Stars: ✭ 42 (-97.68%)
Image To Image Papers🦓<->🦒 🌃<->🌆 A collection of image to image papers with code (constantly updating)
Stars: ✭ 949 (-47.68%)
HyperganComposable GAN framework with api and user interface
Stars: ✭ 1,104 (-39.14%)
Cyclegan QpOfficial PyTorch implementation of "Artist Style Transfer Via Quadratic Potential"
Stars: ✭ 59 (-96.75%)
Esrgan Tf2ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks, published in ECCV 2018) implemented in Tensorflow 2.0+. This is an unofficial implementation. With Colab.
Stars: ✭ 61 (-96.64%)
Acgan PytorchPytorch implementation of Conditional Image Synthesis with Auxiliary Classifier GANs
Stars: ✭ 57 (-96.86%)
Unet SegmentationThe U-Net Segmentation plugin for Fiji (ImageJ)
Stars: ✭ 62 (-96.58%)
Dcgan TensorflowA Tensorflow implementation of Deep Convolutional Generative Adversarial Networks trained on Fashion-MNIST, CIFAR-10, etc.
Stars: ✭ 70 (-96.14%)
Pacgan[NeurIPS 2018] [JSAIT] PacGAN: The power of two samples in generative adversarial networks
Stars: ✭ 67 (-96.31%)
Dstl unetDstl Satellite Imagery Feature Detection
Stars: ✭ 117 (-93.55%)
EdafaTest Time Augmentation (TTA) wrapper for computer vision tasks: segmentation, classification, super-resolution, ... etc.
Stars: ✭ 107 (-94.1%)
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: ✭ 13 (-99.28%)
Data Science Bowl 2018End-to-end one-class instance segmentation based on U-Net architecture for Data Science Bowl 2018 in Kaggle
Stars: ✭ 56 (-96.91%)
Pix2pixImage-to-image translation with conditional adversarial nets
Stars: ✭ 8,765 (+383.19%)
Pytorch FidCompute FID scores with PyTorch.
Stars: ✭ 1,175 (-35.23%)
Vnet TensorflowTensorflow implementation of the V-Net architecture for medical imaging segmentation.
Stars: ✭ 84 (-95.37%)
Ssd PytorchSSD: Single Shot MultiBox Detector pytorch implementation focusing on simplicity
Stars: ✭ 107 (-94.1%)
Mit Deep LearningTutorials, assignments, and competitions for MIT Deep Learning related courses.
Stars: ✭ 8,912 (+391.29%)
Caffe ModelCaffe models (including classification, detection and segmentation) and deploy files for famouse networks
Stars: ✭ 1,258 (-30.65%)
Tensorflow Mnist Cgan CdcganTensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.
Stars: ✭ 122 (-93.27%)
CaloganGenerative Adversarial Networks for High Energy Physics extended to a multi-layer calorimeter simulation
Stars: ✭ 87 (-95.2%)