Dr Yifan Liu's starred repositories
progressive_growing_of_gans
Progressive Growing of GANs for Improved Quality, Stability, and Variation
semantic-segmentation-pytorch
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset
BicycleGAN
Toward Multimodal Image-to-Image Translation
PhotographicImageSynthesis
Photographic Image Synthesis with Cascaded Refinement Networks
openseg.pytorch
The official Pytorch implementation of OCNet series and SegFix.
pose-tensorflow
Human Pose estimation with TensorFlow framework
OCNet.pytorch
Please choose the openseg.pytorch project for the updated code that achieve SOTA on 6 benchmarks!
light-weight-refinenet
Light-Weight RefineNet for Real-Time Semantic Segmentation
structure_knowledge_distillation
The official code for the paper 'Structured Knowledge Distillation for Semantic Segmentation'. (CVPR 2019 ORAL) and extension to other tasks.
GHM_Detection
The implementation of “Gradient Harmonized Single-stage Detector” published on AAAI 2019.
PyTorch-progressive_growing_of_gans
PyTorch implementation of Progressive Growing of GANs for Improved Quality, Stability, and Variation.
erfnet_pytorch
Pytorch code for semantic segmentation using ERFNet
prog_gans_pytorch_inference
PyTorch inference for "Progressive Growing of GANs" with CelebA snapshot
MobileNetV2-pytorch
Impementation of MobileNetV2 in pytorch
GAN-discussions
GAN讨论群讨论汇总的地方
Auto_painter
Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Such an image can be generated at pixel level by learning from a large collection of images. Learning to generate colorful cartoon images from black-and-white sketches is not only an interesting research problem, but also a useful application in digital entertainment. In this paper, we investigate the sketch-to-image synthesis problem by using conditional generative adversarial networks (cGAN). We propose a model called auto-painter which can automatically generate compatible colors given a sketch. Wasserstein distance is used in training cGAN to overcome model collapse and enable the model converged much better. The new model is not only capable of painting hand-draw sketch with compatible colors, but also allowing users to indicate preferred colors. Experimental results on different sketch datasets show that the auto-painter performs better than other existing image-to-image methods.
text-gan-tensorflow
TensorFlow GAN implementation using Gumbel Softmax
Depth_in_The_Wild
Pytorch implementation of Single-Image Depth Perception in the Wild https://arxiv.org/pdf/1604.03901.pdf
Gumbel-Softmax-VAE-in-tensorflow
Semi-Supervised Learning with Categorical VAE (experimented on MNIST)
Tensorflow_DCGAN
Study Friendly Implementation of DCGAN in Tensorflow
IGCV3-pytorch
IGCV3 reimplement by pytorch
HumanPose-tensorflow
as the title