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3D super-resolution using Generative Adversarial Networks
This is the official website of our work 3D Appearance Super-Resolution with Deep Learning published on CVPR2019.
3D Generative Adversarial Network
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
Awesome-pytorch-list 翻译工作进行中......
A curated list of papers & resources linked to 3D reconstruction from images.
中文医疗NLP领域 数据集,论文 ,知识图谱,语料,工具包
PyTorch implementation for Convolutional Networks with Adaptive Inference Graphs
Search by similarity and make sense of ~1500 papers with TF-IDF and t-SNE in Tensorboard.
Repository for "Deformable 3D Convolution for Video Super-Resolution", SPL, 2020
An implementation of BRAIN MRI SUPER RESOLUTION USING 3D DEEP DENSELY CONNECTED NEURAL NETWORKS
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Image reconstruction done with untrained neural networks.
Implementation of some interesting ideas of deeplearning.
Pytorch implementation of Deep Recursive Residual Network for Super Resolution (DRRN), CVPR 2017
PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)
Source code for DCL-Net, a deep learning model for sensorless freehand 3D ultrasound volume reconstruction.
Implementation of Graph Convolutional Networks in TensorFlow
Semi-Supervised classification with Graph Convolution Networks using Pytorch
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral
Repo for the paper "GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects"
Hyperspectral Image Spatial Super-Resolution via 3D-Full-Convolutional-Neural-Network
A paper summary of image inpainting
Code for the paper "Image2Mesh: A Learning Framework for Single Image 3D Reconstruction"
A PyTorch Library for Accelerating 3D Deep Learning Research
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
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.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.