jinwoolim8180 Goto Github PK

amp-net-test

AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing

attention-cs

Memory-Augmented Deep Unfolding Network for Compressive Sensing(ACMMM 2021)

checkered-ising-model

A Fortran implementation of 3D and 2D Ising model with OpenMPI multiprocessing

colored-fl-imbalance

deep-amp

doom-wasm-metaverse

Doom metaverse

dynamic-talking-heads

d

fastmri

federated-learning

An open source FL implement with dataset(Femnist, Shakespeare, MNIST, Cifar-10 and Fashion-Mnist) using pytorch

femnist_pytorch

ffmpeg_camera_streamer

This program can read from PC camera and stream it to live streaming server, you can also add different video filters to the live stream.本程序实现了读取PC端摄像头并进行直播的功能,同时支持添加不同的视频滤镜

fitting-random-labels

Example code for the paper "Understanding deep learning requires rethinking generalization"

fl-baseline

A PyTorch framework for federated learning. This is a very basic framework.

fl-loss-surface

fl-sparse-clients

fl-sparse-masking

go-ethereum

Official Go implementation of the Ethereum protocol

gradient-lottery-ticket

This repository contains a Pytorch implementation of the paper "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" by Jonathan Frankle and Michael Carbin that can be easily adapted to any model/dataset.

lab02_05

litecoin

Litecoin source tree

loamp

AMP algorithm with learned Onsager term

mobiconv

Classification with PyTorch.

mobiconv_imagenet

PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet

opine-net

Optimization-Inspired Compact Deep Compressive Sensing, JSTSP2020 (PyTorch Code)

partitioned-ising-model

pytorch-cifar-mobiconv

95.47% on CIFAR10 with PyTorch

pytorch_resnet_cifar10

Proper implementation of ResNet-s for CIFAR10/100 in pytorch that matches description of the original paper.

recurrent-convnext

Code release for ConvNeXt model

rnntalkinghead

[ICLR 2021 Spotlight] A Good Image Generator Is What You Need for High-Resolution Video Synthesis

split-finetune

[ICLR2022] Efficient Split-Mix federated learning for in-situ model customization during both training and testing time