深度学习资源汇总清单(框架、数据集、期刊会议)

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以爱与青春为名,陪你一路成长

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

深度学习资源汇总清单(框架、数据集、期刊会议)

深度学习资源汇总清单(框架、数据集、期刊会议)


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