人工智能资源集锦

by 【计算机视觉联盟】 王博（Kings）、Sophia

Last updated: 2020/08/29

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Table of Contents

• 【1】Frameworks
• 【2】SDKs
• 【3】Benchmark Datasets
• 【4】Conferences

Most of the time people use different deep learning frameworks and Standard Development Kits (SDKs) for implementing deep learning approaches which are listed below:

A.1. Frameworks

Tensorflow: https://www.tensorflow.org/

Caffe: http://caffe.berkeleyvision.org/

KERAS: https://keras.io/

Theano: http://deeplearning.net/software/theano/

Torch: http://torch.ch/

PyTorch: http://pytorch.org/

Lasagne: https://lasagne.readthedocs.io/en/latest/

DL4J (DeepLearning4J): https://deeplearning4j.org/

Chainer: http://chainer.org/

DIGITS: https://developer.nvidia.com/digits

CNTK (Microsoft): https://github.com/Microsoft/CNTK

MatConvNet: http://www.vlfeat.org/matconvnet/

MINERVA: https://github.com/dmlc/minerva

MXNET: https://github.com/dmlc/mxnet

OpenDeep: http://www.opendeep.org/

PuRine: https://github.com/purine/purine2

PyLerarn2: http://deeplearning.net/software/pylearn2/

TensorLayer: https://github.com/zsdonghao/tensorlayer

LBANN: https://github.com/LLNL/lbann

A.2. SDKs

cuDNN: https://developer.nvidia.com/cudnn

TensorRT: https://developer.nvidia.com/tensorrt

DeepStreamSDK: https://developer.nvidia.com/deepstream-sdk

cuBLAS: https://developer.nvidia.com/cublas

cuSPARSE: http://docs.nvidia.com/cuda/cusparse/

NCCL: https://devblogs.nvidia.com/parallelforall/fast-multi-gpu-collectives-nccl/

A.3. Benchmark Datasets

Here is the list of benchmark datasets that are used often to evaluate deep learning approaches in different domains of application:

A.3.1. Image Classification or Detection or Segmentation

List of datasets are used in the field of image processing and computer vision:

MNIST: http://yann.lecun.com/exdb/mnist/

CIFAR 10/100: https://www.cs.toronto.edu/~kriz/cifar.html

SVHN/ SVHN2: http://ufldl.stanford.edu/housenumbers/

CalTech 101/256: http://www.vision.caltech.edu/Image_Datasets/Caltech101/

STL-10: https://cs.stanford.edu/~acoates/stl10/

NORB: http://www.cs.nyu.edu/~ylclab/data/norb-v1.0/

SUN-dataset: http://groups.csail.mit.edu/vision/SUN/

ImageNet: http://www.image-net.org/

National Data Science Bowl Competition: http://www.datasciencebowl.com/

COIL 20/100: http://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php

MS COCO DATASET: http://mscoco.org/

MIT-67 scene dataset: http://web.mit.edu/torralba/www/indoor.html

Caltech-UCSD Birds-200 dataset: http://www.vision.caltech.edu/visipedia/CUB-200-2011.html

Pascal VOC 2007 dataset: http://host.robots.ox.ac.uk/pascal/VOC/voc2007/

H3D Human Attributes dataset: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/shape/poselets/

Face recognition dataset: http://vis-www.cs.umass.edu/lfw/

For more data-set visit: https://www.kaggle.com/

http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm

Recently Introduced Datasets in Sept. 2016: Google Open Images (~9M images)—https://github.com/openimages/dataset

Youtube-8M (8M videos: https://research.google.com/youtube8m/

A.3.2. Text Classification

Reuters-21578 Text Categorization Collection: http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html

Sentiment analysis from Stanford: http://ai.stanford.edu/~amaas/data/sentiment/

Movie sentiment analysis from Cornel： http://www.cs.cornell.edu/people/pabo/movie-review-data/

A.3.3. Language Modeling

Free eBooks: https://www.gutenberg.org/

Brown and stanford corpus on present americal english: https://en.wikipedia.org/wiki/Brown_Corpus

Google 1Billion word corpus: https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark

A.3.4. Image Captioning

Flickr-8k: http://nlp.cs.illinois.edu/HockenmaierGroup/8k-pictures.html

Flickr-30k Common Objects in Context (COCO): http://cocodataset.org/#overview, http://sidgan.me/technical/2016/01/09/Exploring-Datasets

A.3.5. Machine Translation

Pairs of sentences in English and French: https://www.isi.edu/natural-language/download/hansard/

European Parliament Proceedings parallel Corpus 196-2011: http://www.statmt.org/europarl/

The statistics for machine translation: http://www.statmt.org/

A.3.6. Question Answering

Stanford Question Answering Dataset (SQuAD): https://rajpurkar.github.io/SQuAD-explorer/

Dataset from DeepMind: https://github.com/deepmind/rc-data

Amazon dataset: http://jmcauley.ucsd.edu/data/amazon/qa/,

http://trec.nist.gov/data/qamain...,

http://www.ark.cs.cmu.edu/QA-data/,

http://webscope.sandbox.yahoo.co...,

http://blog.stackoverflow.com/20..

A.3.7. Speech Recognition

TIMIT: https://catalog.ldc.upenn.edu/LDC93S1

Voxforge: http://voxforge.org/

Open Speech and Language Resources: http://www.openslr.org/12/

A.3.8. Document Summarization https://archive.ics.uci.edu/ml/datasets/Legal+Case+Reports

http://www-nlpir.nist.gov/related_projects/tipster_summac/cmp_lg.html

https://catalog.ldc.upenn.edu/LDC2002T31

A.3.9. Sentiment Analysis:

IMDB dataset: http://www.imdb.com/

A.3.10. Hyperspectral Image Analysis

http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes

https://engineering.purdue.edu/~biehl/MultiSpec/hyperspectral.html

http://www2.isprs.org/commissions/comm3/wg4/HyRANK.html

In addition, there is another alternative solution in data programming that labels subsets of data using weak supervision strategies or domain heuristics as labeling functions even if they are noisy and may conflict samples [87].

A.4. Journals and Conferences

In general, researchers publish their primary version of research on the ArXiv (https://arxiv.org/). Most of the conferences have been accepting papers on Deep learning and its related field. Popular conferences are listed below:

A.4.1. Conferences

Neural Information Processing System (NIPS)

International Conference on Learning Representation (ICLR): What are you doing for Deep Learning?

International Conference on Machine Learning (ICML)

Computer Vision and Pattern Recognition (CVPR): What are you doing with Deep Learning?

International Conference on Computer Vision (ICCV)

European Conference on Computer Vision (ECCV)

British Machine Vision Conference (BMVC)

A.4.2. Journal

Journal of Machine Learning Research (JMLR)

IEEE Transaction of Neural Network and Learning System (ITNNLS)

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

Computer Vision and Image Understanding (CVIU)

Pattern Recognition Letter

Neural Computing and Application

International Journal of Computer Vision

IEEE Transactions on Image Processing

IEEE Computational Intelligence Magazine

Proceedings of IEEE

IEEE Signal Processing Magazine

Neural Processing Letter

Pattern Recognition

Neural Networks

ISPPRS Journal of Photogrammetry and Remote Sensing

A.4.3. Tutorials on Deep Learning

http://deeplearning.net/tutorial/

http://deeplearning.stanford.edu/tutorial/

http://deeplearning.net/tutorial/deeplearning.pdf

Courses on Reinforcement Learning: http://rll.berkeley.edu/deeprlcourse/

A.4.4. Books on Deep Learning

https://github.com/HFTrader/DeepLearningBook

https://github.com/janishar/mit-deep-learning-book-pdf

http://www.deeplearningbook.org/

【1】开源框架

1. Deepnet
2. Deeppy
3. JavaNN
4. hebel
5. Mocha.jl
6. OpenDL
7. cuDNN
8. MGL
9. Knet.jl
10. Nvidia DIGITS - a web app based on Caffe
11. Neon - Python based Deep Learning Framework
12. Keras - Theano based Deep Learning Library
13. Chainer - A flexible framework of neural networks for deep learning
14. RNNLM Toolkit
15. RNNLIB - A recurrent neural network library
16. char-rnn
17. MatConvNet: CNNs for MATLAB
18. Minerva - a fast and flexible tool for deep learning on multi-GPU
19. Brainstorm - Fast, flexible and fun neural networks.
20. Tensorflow - Open source software library for numerical computation using data flow graphs
21. Caffe
22. Torch7
23. Theano
24. cuda-convnet
25. convetjs
26. Ccv
27. NuPIC
28. DeepLearning4J
29. Brain
30. DeepLearnToolbox
31. DMTK - Microsoft Distributed Machine Learning Tookit
32. Scikit Flow - Simplified interface for TensorFlow (mimicking Scikit Learn)
33. MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework
34. Veles - Samsung Distributed machine learning platform
35. Marvin - A Minimalist GPU-only N-Dimensional ConvNets Framework
36. Apache SINGA - A General Distributed Deep Learning Platform
37. DSSTNE - Amazon's library for building Deep Learning models
38. SyntaxNet - Google's syntactic parser - A TensorFlow dependency library
39. mlpack - A scalable Machine Learning library
40. Torchnet - Torch based Deep Learning Library
41. Paddle - PArallel Distributed Deep LEarning by Baidu
42. NeuPy - Theano based Python library for ANN and Deep Learning
43. Lasagne - a lightweight library to build and train neural networks in Theano
44. nolearn - wrappers and abstractions around existing neural network libraries, most notably Lasagne
45. Sonnet - a library for constructing neural networks by Google's DeepMind
46. PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
47. CNTK - Microsoft Cognitive Toolkit
48. Serpent.AI - Game agent framework: Use any video game as a deep learning sandbox
49. Caffe2 - A New Lightweight, Modular, and Scalable Deep Learning Framework
50. deeplearn.js - Hardware-accelerated deep learning and linear algebra (NumPy) library for the web
51. TensorForce - A TensorFlow library for applied reinforcement learning
52. Coach - Reinforcement Learning Coach by Intel® AI Lab
53. albumentations - A fast and framework agnostic image augmentation library

研究学者推荐

• Galen Andrew
• Geoffrey Hinton
• George Dahl
• Graham Taylor
• Grégoire Montavon
• Guido Francisco Montúfar
• Guillaume Desjardins
• Hannes Schulz
• Hélène Paugam-Moisy
• Honglak Lee
• Hugo Larochelle
• Ilya Sutskever
• Itamar Arel
• James Martens
• Jason Morton
• Jason Weston
• Jeff Dean
• Jiquan Mgiam
• Joseph Turian
• Joshua Matthew Susskind
• Jürgen Schmidhuber
• Justin A. Blanco
• Koray Kavukcuoglu
• KyungHyun Cho
• Li Deng
• Lucas Theis
• Ludovic Arnold
• Marc'Aurelio Ranzato
• Martin Längkvist
• Misha Denil
• Mohammad Norouzi
• Nando de Freitas
• Navdeep Jaitly
• Nicolas Le Roux
• Nitish Srivastava
• Noel Lopes
• Oriol Vinyals
• Aaron Courville
• Abdel-rahman Mohamed
• Adam Coates
• Alex Acero
• Alex Krizhevsky
• Alexander Ilin
• Amos Storkey
• Andrej Karpathy
• Andrew M. Saxe
• Andrew Ng
• Andrew W. Senior
• Andriy Mnih
• Ayse Naz Erkan
• Benjamin Schrauwen
• Bernardete Ribeiro
• Bo David Chen
• Boureau Y-Lan
• Brian Kingsbury
• Christopher Manning
• Clement Farabet
• Dan Claudiu Cireșan
• David Reichert
• Derek Rose
• Dong Yu
• Drausin Wulsin
• Erik M. Schmidt
• Eugenio Culurciello
• Frank Seide
• Pascal Vincent
• Patrick Nguyen
• Pedro Domingos
• Peggy Series
• Pierre Sermanet
• Piotr Mirowski
• Quoc V. Le
• Reinhold Scherer
• Richard Socher
• Rob Fergus
• Robert Coop
• Robert Gens
• Roger Grosse
• Ronan Collobert
• Ruslan Salakhutdinov
• Sebastian Gerwinn
• Stéphane Mallat
• Sven Behnke
• Tapani Raiko
• Tara Sainath
• Tijmen Tieleman
• Tom Karnowski
• Tomáš Mikolov
• Ueli Meier
• Vincent Vanhoucke
• Volodymyr Mnih
• Yann LeCun
• Yichuan Tang
• Yoshua Bengio
• Yotaro Kubo
• Youzhi (Will) Zou
• Fei-Fei Li
• Ian Goodfellow
• Robert Laganière