brisker

google-research

Google Research

pwc

This repository is no longer maintained.

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

transferlearning

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

autokeras

AutoML library for deep learning

tensorpack

A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility

graph_nets

Build Graph Nets in Tensorflow

awesome-automl-papers

A curated list of automated machine learning papers, articles, tutorials, slides and projects

awesome-knowledge-distillation

Awesome Knowledge Distillation

Awesome-offer

关于面试/谈Offer/程序员职场生涯等

rethinking-network-pruning

Rethinking the Value of Network Pruning (Pytorch) (ICLR 2019)

blocksparse

Efficient GPU kernels for block-sparse matrix multiplication and convolution

food-101-keras

Food Classification with Deep Learning in Keras / Tensorflow

Deep-Compression-AlexNet

Deep Compression on AlexNet

DepthwiseConvolution

A personal depthwise convolution layer implementation on caffe by liuhao.(only GPU)

quantized_distillation

Implements quantized distillation. Code for our paper "Model compression via distillation and quantization"

caffe

Ristretto: Caffe-based approximation of convolutional neural networks.

label-refinery

Label Refinery: Improving ImageNet Classification through Label Progression

LQ-Nets

LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks

Incremental-Network-Quantization

Caffe Implementation for Incremental network quantization

beauty-net

A simple, flexible, and extensible template for PyTorch. It's beautiful.

Bi-Real-net

Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm. In ECCV 2018 and IJCV

caffe-augmentation

Data Augmentation for Caffe

DAN

Code release of "Learning Transferable Features with Deep Adaptation Networks" (ICML 2015)

MobileNetv2

Focal-Loss

loss layer of implementation

hwgq

Caffe implementation of accurate low-precision neural networks

ternarynet

Implementation for Trained Ternary Network.

SYQ

This repository represents training examples for the CVPR 2018 paper "SYQ:Learning Symmetric Quantization For Efficient Deep Neural Networks"

Two-Step-Quantization-AlexNet

Two-Step Quantization on AlexNet