zqcolorful Goto Github PK

3d-gan-superresolution

3D super-resolution using Generative Adversarial Networks

3d_appearance_sr

This is the official website of our work 3D Appearance Super-Resolution with Deep Learning published on CVPR2019.

3dgan-release

3D Generative Adversarial Network

3dsrcnn-pytorch

awesome-pytorch-list

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

awesome-pytorch-list-cnversion

Awesome-pytorch-list 翻译工作进行中......

awesome_3dreconstruction_list

A curated list of papers & resources linked to 3D reconstruction from images.

chinese_medical_nlp

中文医疗NLP领域 数据集，论文 ，知识图谱，语料，工具包

convnet-aig

PyTorch implementation for Convolutional Networks with Adaptive Inference Graphs

cvpr2020-visualizer

Search by similarity and make sense of ~1500 papers with TF-IDF and t-SNE in Tensorboard.

d3dnet

Repository for "Deformable 3D Convolution for Video Super-Resolution", SPL, 2020

dcsrn

An implementation of BRAIN MRI SUPER RESOLUTION USING 3D DEEP DENSELY CONNECTED NEURAL NETWORKS

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deep_image_prior

Image reconstruction done with untrained neural networks.

deeplearningforfun

Implementation of some interesting ideas of deeplearning.

drrn-pytorch

Pytorch implementation of Deep Recursive Residual Network for Super Resolution (DRRN), CVPR 2017

edsr-pytorch

PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

freehandusrecon

Source code for DCL-Net, a deep learning model for sensorless freehand 3D ultrasound volume reconstruction.

gcn

Implementation of Graph Convolutional Networks in TensorFlow

gcn-demo

Semi-Supervised classification with Graph Convolution Networks using Pytorch

generative-models

Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

generative-models-1

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

generative_inpainting

DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral

geometrics

Repo for the paper "GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects"

hyperspectral-image-spatial-super-resolution-via-3d-full-convolutional-neural-network

Hyperspectral Image Spatial Super-Resolution via 3D-Full-Convolutional-Neural-Network

image-inpainting

A paper summary of image inpainting

image2mesh

Code for the paper "Image2Mesh: A Learning Framework for Single Image 3D Reconstruction"

kaolin

A PyTorch Library for Accelerating 3D Deep Learning Research

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

maxout-pytorch

Here is the implementation of Maxout Layer from paper: https://arxiv.org/pdf/1302.4389.pdf in PyTorch. In forward pass, maxout for the feature maps of CNN is calculated. For backward pass, derivative of error w.r.t input is only propagated through the cells of feature maps array which were activated in forward pass(which is derivative of Maxout). To be sure this works fine, results were compared with Maxout in Theano(which is with the name featurepool layer) and were confirmed to be working fine. To make it work according to your desire, just change max_out parameter in forward function. The code is very efficient and fast.