深度学习资源汇总清单（框架、数据集、期刊会议）

大多数时候，人们使用不同的深度学习框架和标准开发工具箱。(SDKs)，用于实施深度学习方法，具体如下：

框架

• 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

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/

基准数据集

以下是常用于评估不同应用领域的深度学习方法的基准数据集列表。

图像分类或检测或分割

• 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/

文本分类

• 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/ 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-wordlanguage- modeling-benchmark

图像编码

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

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

机器翻译

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

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

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

问答

• Stanford Question Answering Dataset (SQuAD): https://rajpurkar.github.io/SQuADexplorer/

• 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..

语音辨识

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

• Voxforge: http://voxforge.org/

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

文章摘要

• 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

情感分析

• IMDB dataset: http://www.imdb.com/

高光谱图像分析

• 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

期刊和会议

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)

Journal

• Journal of Machine Learning Research (JMLR)

• IEEE Transaction of Neural Network and Learning System (

• 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