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- OpenCV 3.3版本发布
- 鱼和熊掌兼得，DNN加入 OpenCV 全家桶 | 知乎专栏
- Qualcomm Snapdragon Neural Processing Engine (NPE) | Qualcomm Developer Network
- AI让芯片业洗牌: 苹果、微软和谷歌挤入赛道，英特尔、英伟达、高通、AMD几家欢乐几家愁 | 新智元
- 解密图森：英伟达为何投资这家无人车公司；估值18亿背后有位长者 | 量子位
- 被英伟达相中，给Tier1供货，天瞳威视仅靠AI就搞定ADAS | 车东西
- ARM的最新NB-IoT报告 | 5G
- ARM发飙！几个月后手机处理器将因它们而变天！ | 智趣狗
- 人工智能和云计算让芯片业洗牌，英特尔成了最大输家 | 量子位
- The Rise of AI Is Forcing Google and Microsoft to Become Chipmakers | WIRED
- 如何评价腾讯刚出的ncnn库？ | 知乎
- 沈向洋宣布微软开发 AI 芯片HPU，剑指英伟达等芯片巨头软肋 | 新智元
- 超越GPU，FPGA、ASIC和更智能的手机 ｜ 新智元
- "TensorFire - runs neural networks in the browser using WebGL" [Demo: style-transfer]
- Getting Started with Neural Compute Stick and Rasbperry Pi 3 | YouTube
- [CVPR2017] Squeeze-and-Excitation networks (ILSVRC 2017 winner) at CVPR2017
- [1707.06990] Memory-Efficient Implementation of DenseNets
- BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks
- Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing [code]
- [1704.06904] Residual Attention Network for Image Classification [code]
- [1707.09102] Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization
- [1708.00999] Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding Learning
- [1608.01409] Faster CNNs with Direct Sparse Convolutions and Guided Pruning
- [1606.05316] Learning Infinite-Layer Networks: Without the Kernel Trick
- [1707.09422] Hyperprofile-based Computation Offloading for Mobile Edge Networks
- [1705.04630] Forecasting using incomplete models
- [1707.09068] Tartan: Accelerating Fully-Connected and Convolutional Layers in Deep Learning Networks by Exploiting Numerical Precision Variability
- [1707.09926] A Framework for Super-Resolution of Scalable Video via Sparse Reconstruction of Residual Frames
- [1707.09855] Convolution with Logarithmic Filter Groups for Efficient Shallow CNN
- [1707.09597] ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images
- [ASPLOS’17] Neurosurgeon: Collaborative intelligence between the cloud and mobile edge
- [1604.08772] Towards Conceptual Compression
- [1608.02893] Syntactically Informed Text Compression with Recurrent Neural Networks
- [1608.05148] Full Resolution Image Compression with Recurrent Neural Networks
- [CVPR2017] Local Binary Convolutional Neural Networks [code]
- [1703.09746] Coordinating Filters for Faster Deep Neural Networks
- [1707.08005] Towards Evolutional Compression
- [ICML2017] Analytical Guarantees on Numerical Precision of Deep Neural Networks
网络压缩
- yonghenglh6/DepthwiseConvolution: A personal mobile convolution implementation on caffe by liuhao.(only GPU)
- liuzhuang13/DenseNet: Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award)
- kevinzakka/DenseNet: PyTorch Implementation of "Densely Connected Convolutional Networks"
- hollance/MobileNet-CoreML: The MobileNet neural network using Apple's new CoreML framework
- AngusG/tensorflow-xnor-bnn: BinaryNets in TensorFlow with XNOR GEMM op
- jonathanmarek1/binarynet-tensorflow
- farmingyard/caffe-mobilenet: A caffe implementation of mobilenet's depthwise convolution layer
- kedartatwawadi/NN_compression
- chuanqi305/MobileNet-SSD: Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.
性能
- hollance/BNNS-vs-MPSCNN: Compares the speed of Apple's two deep learning frameworks: BNNS and Metal Performance Shaders
- DeepMark/deepmark: THE Deep Learning Benchmarks
模型加密
增强现实
- ProjectDent/ARKit-CoreLocation: Combines the high accuracy of AR with the scale of GPS data
- bjarnel/arkit-tictactoe: Tic-Tac-Toe implemented using ARKit+Scenekit
- arirawr/ARKit-FloorIsLava: Basic ARKit example that detects planes and makes them lava.
- exyte/ARTetris: Augmented Reality Tetris made with ARKit and SceneKit
- bjarnel/arkit-portal: Simple portal demo implemented with ARKit+SceneKit, the trick is to change the rendering order and render invisible "masks" to hide what's inside.
- bjarnel/scenekit-tictactoe
安卓
iOS
- kingreza/SeeFood: Inspired by HBO's Silicon Valley: SeeFood is an iOS app that uses CoreML to detect various dishes
- hollance/TensorFlow-iOS-Example: Source code for my blog post "Getting started with TensorFlow on iOS"
- Naituw/CoreMLDemo: Demo for CoreML & Vision Framework
模型应用
- msracver/FCIS: Fully Convolutional Instance-aware Semantic Segmentation
- bearpaw/PyraNet: Code for "Learning Feature Pyramids for Human Pose Estimation" (ICCV 2017)
- aquaviter/iot-demo-mxnet-greengrass
- bearpaw/PyraNet: Code for "Learning Feature Pyramids for Human Pose Estimation" (ICCV 2017)
- CongWeilin/mtcnn-caffe: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks
- foreverYoungGitHub/MTCNN: Repository for "Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks", implemented with Caffe, C++ interface.
- OAID/mtcnn: C++ project to implement MTCNN, a perfect face detect algorithm, on different DL frameworks. The most popular frameworks: caffe/mxnet/tensorflow, are all suppported now
- Seanlinx/mtcnn: this repository is the implementation of MTCNN in MXnet
- LaoDar/cnn_head_pose_estimator: a simple and fast mxnet version CNN based head pose estimator
加速库/框架
- Darknet with NNPACK: NNPACK was used to optimize Darknet without using a GPU. It is useful for embedded devices using ARM CPUs
- naibaf7/libdnn: Greentea LibDNN - a universal convolution implementation supporting CUDA and OpenCL
- blei-lab/edward: A library for probabilistic modeling, inference, and criticism. Deep generative models, variational inference. Runs on TensorFlow
- dmlc/nnvm-fusion: Kernel Fusion and Runtime Compilation Based on NNVM
音频图像视频处理
- MTG/essentia: C++ library for audio and music analysis, description and synthesis, including Python bindings
- Pili-完美直播体验（Pili Streaming Cloud）
- pili-engineering/PLDroidMediaStreaming: PLDroidMediaStreaming 是 Pili 直播 SDK 的 Android 推流端，支持 RTMP 推流，h.264 和 AAC 编码，硬编、软编支持。具有丰富的数据和状态回调，方便用户根据自己的业务定制化开发。具有直播场景下的重要功能，如：美颜、背景音乐、水印等功能。PLDroidMediaStreaming 是现在目前重点维护的版本，自带采集模块也支持用户自己做采集端。
- pili-engineering/PLDroidShortVideo: PLDroidShortVideo 是七牛推出的一款适用于 Android 平台的短视频 SDK，提供了包括美颜、滤镜、水印、断点录制、分段回删、视频编辑、混音特效、本地/云端存储在内的多种功能，支持高度定制以及二次开发。
- pili-engineering/PLDroidPlayer: PLDroidPlayer 是 Pili 直播 SDK 的安卓播放器。支持所有直播常用的格式，如：RTMP、HLS、FLV。拥有优秀的功能和特性，如：首屏秒开、追帧优化、丰富的数据和状态回调、硬解软解支持。而且可以根据自己的业务进行高度定制化开发。
- pili-engineering/PLMediaStreamingKit: PLMediaStreamingKit 是 Pili 直播 SDK 的 iOS 推流端，支持 RTMP 推流，h.264 和 AAC 编码，硬编、软编支持。具有丰富的数据和状态回调，方便用户根据自己的业务定制化开发。具有直播场景下的重要功能，如：美颜、背景音乐、水印等功能。
- pili-engineering/PLShortVideoKit: PLShortVideoKit 是七牛推出的一款适用于 iOS 平台的短视频 SDK，提供了包括美颜、滤镜、水印、断点录制、分段回删、视频编辑、混音特效、本地/云端存储在内的多种功能，支持高度定制以及二次开发。
- pili-engineering/PLPlayerKit: PLPlayerKit 是 Pili 直播 SDK 的 iOS 播放器。支持所有直播常用的格式，如：RTMP、HLS、FLV。拥有优秀的功能和特性，如：首屏秒开、追帧优化、丰富的数据和状态回调、硬解软解支持。而且可以根据自己的业务进行高度定制化开发。
- pili-engineering/PLPlayerKit: PLPlayerKit 是 Pili 直播 SDK 的 iOS 播放器。支持所有直播常用的格式，如：RTMP、HLS、FLV。拥有优秀的功能和特性，如：首屏秒开、追帧优化、丰富的数据和状态回调、硬解软解支持。而且可以根据自己的业务进行高度定制化开发。
其它
- facebook/fb-caffe-exts: Some handy utility libraries and tools for the Caffe deep learning framework.
- Samsung/iotjs: Platform for Internet of Things with JavaScript code
- hollance/Forge: A neural network toolkit for Metal
- christopher5106/FastAnnotationTool: A tool using OpenCV to annotate images for image classification, optical character reading, etc.
- raphui/rnk: rnk is a RTOS targeting ARM architecture.
- Tutorial on Hardware Architectures for Deep Neural Networks | MIT MICRO-50
- 基于mtcnn和facenet的实时人脸检测与识别系统开发 | 知乎专栏
- Creating insanely fast image classifiers with MobileNet in TensorFlow | HACKERNOON
- How to squeeze the most from your training data | KDNUGGETS
- Ubuntu16.04腾讯NCNN框架入门到应用 | CSDN
- Building Cross-Platform CUDA Applications with CMake | NVIDIA
- Caffe2 Bay Area Meetup (5/31/2017) | YouTube