junkwhinger's starred repositories
Deep-Reinforcement-Learning-Algorithms-with-PyTorch
PyTorch implementations of deep reinforcement learning algorithms and environments
siamese-triplet
Siamese and triplet networks with online pair/triplet mining in PyTorch
CRAFT-pytorch
Official implementation of Character Region Awareness for Text Detection (CRAFT)
fast-autoaugment
Official Implementation of 'Fast AutoAugment' in PyTorch.
variational-autoencoder
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
Pytorch-Project-Template
A scalable template for PyTorch projects, with examples in Image Segmentation, Object classification, GANs and Reinforcement Learning.
StyleGAN_PyTorch
The implementation of StyleGAN on PyTorch 1.0.1
rl_algorithms
Structural implementation of RL key algorithms
Recycle-GAN
Unsupervised Video Retargeting (e.g. face to face, flower to flower, clouds and winds, sunrise and sunset)
CARN-pytorch
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network (ECCV 2018)
deepaugment
Discover augmentation strategies tailored for your dataset
rl-paper-study
Reinforcement Learning paper review study
QATM_pytorch
Pytorch Implementation of QATM:Quality-Aware Template Matching For Deep Learning
StyleGAN-PyTorch
A simple implementation of Style-Based Generator Architecture for Generative Adversarial Networks with pytorch
mlwpy_code
Code from the Pearson Addison-Wesley book Machine Learning with Python for Everyone
python-photo-mosaic
Python script for creating photomosaic images
soft-Q-learning
discrete soft Q learning(SQL) and soft Q imitation learning(SQIL) implementation in pytorch, simple!
python-korean-handler
python3.x 에서 작동하는 한글 초성, 중성, 종성 분해
fast-style-transfer-tutorial-pytorch
Simple Tutorials & Code Implementation of fast-style-transfer(Perceptual Losses for Real-Time Style Transfer and Super-Resolution, 2016 ECCV) using PyTorch.
XDoG-Python
A Python implementation of eXtended Difference of Gaussians.
pytorch-gqn
Neural Scene Rendering! With Helpful Comments and a video ->