ResNeSt: Split-Attention Networks

style transfer code for generating stylized images using content and style loss from different layers, from different loss networks (vgg-16, vgg-19, inception-v1, inception-v2, inception-v3 trained…

This is a SE_DenseNet which contains a senet (Squeeze-and-Excitation Networks by Jie Hu, Li Shen, and Gang Sun) module, written in Pytorch, train, and eval codes have been released.

Google Brain AutoML

Pytorch implementation of Fast Style Transfer

AI Roadmap:机器学习(Machine Learning)、深度学习(Deep Learning)、对抗神经网络(GAN），图神经网络（GNN），NLP，大数据相关的发展路书(roadmap), 并附海量源码（python，pytorch）带大家消化基本知识点，突破面试，完成从新手到合格工程师的跨越，其中深度学习相关论文附有tensorflow caffe官方源码，应用部分含推荐算法…

Pytorch implementation of our paper accepted by CVPR 2020 (Oral) -- HRank: Filter Pruning using High-Rank Feature Map

Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.

风格迁移三部曲

Papers for CNN, object detection, keypoint detection, semantic segmentation, medical image processing, SLAM, etc.

A curated list of neural network pruning resources.

Keras model convolutional filter pruning package

An optimizer that trains as fast as Adam and as good as SGD.

Neural Style implementation in PyTorch! 🎨

Code and data for paper: https://arxiv.org/abs/1802.07101

Fast Neural Style Transfer with Arbitrary Style using AdaIN Layer - Based on Huang et al. "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization"

Fast Neural Style Transfer implementation in PyTorch 🎨 🎨 🎨

CS231N Final Project

model compression based on pytorch (1、quantization: 8/4/2bits(dorefa)、ternary/binary value(twn/bnn/xnor-net)；2、 pruning: normal、regular and group convolutional channel pruning；3、 group convolution …

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

PyTorch implementation of "Pruning Filters For Efficient ConvNets"

Style transfer using WCT transforms

Network Slimming (Pytorch) (ICCV 2017)

Depth-Preserving Style Transfer

记录cv算法工程师的成长之路，分享计算机视觉和模型压缩部署技术栈笔记。https://harleyszhang.github.io/cv_note/

PyTorch implementation of "Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration"

Software that can perform photorealistic style transfer without the need of any post-processing steps.

周志华《机器学习》又称西瓜书是一本较为全面的书籍，书中详细介绍了机器学习领域不同类型的算法(例如：监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等)，记录了本人在学习过程中的理解思路与扩展知识点，希望对新人阅读西瓜书有所帮助！

计算机视觉项目实战