深度学习领域资源汇总清单

  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