「DeepLearning」深度学习应用

「DeepLearning」深度学习应用

自然语言处理 (NLP)


[1] ★★★★

Antoine Bordes, et al. “Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing.” AISTATS(2012)

https://www.hds.utc.fr/%7Ebordesan/dokuwiki/lib/exe/fetch.php?id=en%3Apubli&cache=cache&media=en:bordes12aistats.pdf

[2] ★★★

word2vec

Mikolov, et al. “Distributed representations of words and phrases and their compositionality.” ANIPS(2013): 3111-3119

http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf

[3]★★★

Sutskever, et al. “Sequence to sequence learning with neural networks.” ANIPS(2014)

http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf

[4] ★★★★

Ankit Kumar, et al. “Ask Me Anything: Dynamic Memory Networks for Natural Language Processing.” arXiv preprint arXiv:1506.07285(2015)

https://arxiv.org/abs/1506.07285

[5] ★★★★

Yoon Kim, et al. “Character-Aware Neural Language Models.” NIPS(2015) arXiv preprint arXiv:1508.06615(2015)

https://arxiv.org/abs/1508.06615

[6] bAbI任务 ★★★

Jason Weston, et al. “Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks.” arXiv preprint arXiv:1502.05698(2015)

https://arxiv.org/abs/1502.05698

[7] CNN / DailyMail 风格对比 ★★

Karl Moritz Hermann, et al. “Teaching Machines to Read and Comprehend.” arXiv preprint arXiv:1506.03340(2015)

https://arxiv.org/abs/1506.03340

[8] 当前最先进的文本分类 ★★★

Alexis Conneau, et al. “Very Deep Convolutional Networks for Natural Language Processing.” arXiv preprint arXiv:1606.01781(2016)

https://arxiv.org/abs/1606.01781

[9] 稍次于最先进方案,但速度快很多 ★★★

Armand Joulin, et al. “Bag of Tricks for Efficient Text Classification.” arXiv preprint arXiv:1607.01759(2016)

https://arxiv.org/abs/1607.01759

目标检测


[1] ★★★

Szegedy, Christian, Alexander Toshev, and Dumitru Erhan. “Deep neural networks for object detection.” Advances in Neural Information Processing Systems. 2013.

http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf

[2] RCNN ★★★★★

Girshick, Ross, et al. “Rich feature hierarchies for accurate object detection and semantic segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.

http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf

[3] SPPNet ★★★★

He, Kaiming, et al. “Spatial pyramid pooling in deep convolutional networks for visual recognition.” European Conference on Computer Vision. Springer International Publishing, 2014.

http://arxiv.org/pdf/1406.4729

[4] ★★★

Girshick, Ross. “Fast r-cnn.” Proceedings of the IEEE International Conference on Computer Vision. 2015.

https://pdfs.semanticscholar.org/8f67/64a59f0d17081f2a2a9d06f4ed1cdea1a0ad.pdf

[5] ★★★

Ren, Shaoqing, et al. “Faster R-CNN: Towards real-time object detection with region proposal networks.” Advances in neural information processing systems. 2015.

http://papers.nips.cc/paper/5638-analysis-of-variational-bayesian-latent-dirichlet-allocation-weaker-sparsity-than-map.pdf

[6] 相当实用的YOLO项目 ★★★★★

Redmon, Joseph, et al. “You only look once: Unified, real-time object detection.” arXiv preprint arXiv:1506.02640 (2015).

http://homes.cs.washington.edu/%7Eali/papers/YOLO.pdf

[7] ★★★

Liu, Wei, et al. “SSD: Single Shot MultiBox Detector.” arXiv preprint arXiv:1512.02325 (2015).

http://arxiv.org/pdf/1512.02325

[8] ★★★

Dai, Jifeng, et al. “R-FCN: Object Detection via Region-based Fully Convolutional Networks.” arXiv preprint arXiv:1605.06409 (2016).

https://arxiv.org/abs/1605.06409

[9] ★★★

He, Gkioxari, et al. “Mask R-CNN” arXiv preprint arXiv:1703.06870 (2017).

https://arxiv.org/abs/1703.06870

视觉追踪


[1] 第一份采用深度学习的视觉追踪论文,DLT追踪器 ★★★

Wang, Naiyan, and Dit-Yan Yeung. “Learning a deep compact image representation for visual tracking.” Advances in neural information processing systems. 2013.

http://papers.nips.cc/paper/5192-learning-a-deep-compact-image-representation-for-visual-tracking.pdf

[2] SO-DLT ★★★

Wang, Naiyan, et al. “Transferring rich feature hierarchies for robust visual tracking.” arXiv preprint arXiv:1501.04587 (2015).

http://arxiv.org/pdf/1501.04587

[3] FCNT ★★★

Wang, Lijun, et al. “Visual tracking with fully convolutional networks.” Proceedings of the IEEE International Conference on Computer Vision. 2015.

http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Wang_Visual_Tracking_With_ICCV_2015_paper.pdf

[4] 跟深度学习一样快的非深度学习方法,GOTURN ★★★

Held, David, Sebastian Thrun, and Silvio Savarese. “Learning to Track at 100 FPS with Deep Regression Networks.” arXiv preprint arXiv:1604.01802 (2016).

http://arxiv.org/pdf/1604.01802

[5] 新的最先进的实时目标追踪方案 SiameseFC ★★★

Bertinetto, Luca, et al. “Fully-Convolutional Siamese Networks for Object Tracking.” arXiv preprint arXiv:1606.09549 (2016).

https://arxiv.org/pdf/1606.09549

[6] C-COT ★★★

Martin Danelljan, Andreas Robinson, Fahad Khan, Michael Felsberg. “Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking.” ECCV (2016)

http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/C-COT_ECCV16.pdf

[7] VOT2016大赛冠军 TCNN ★★★

Nam, Hyeonseob, Mooyeol Baek, and Bohyung Han. “Modeling and Propagating CNNs in a Tree Structure for Visual Tracking.” arXiv preprint arXiv:1608.07242 (2016).

https://arxiv.org/pdf/1608.07242

图像标注


[1] ★★★

Farhadi,Ali,etal. “Every picture tells a story: Generating sentences from images”. In Computer VisionECCV 201match0. Spmatchringer Berlin Heidelberg:15-29, 2010.

https://www.cs.cmu.edu/%7Eafarhadi/papers/sentence.pdf

[2] ★★★

Kulkarni, Girish, et al. “Baby talk: Understanding and generating image descriptions”. In Proceedings of the 24th CVPR, 2011.

http://tamaraberg.com/papers/generation_cvpr11.pdf

[3] ★★★

Vinyals, Oriol, et al. “Show and tell: A neural image caption generator”. In arXiv preprint arXiv:1411.4555, 2014.

https://arxiv.org/pdf/1411.4555.pdf

[4] RNN视觉识别与标注

Donahue, Jeff, et al. “Long-term recurrent convolutional networks for visual recognition and description”. In arXiv preprint arXiv:1411.4389 ,2014.

https://arxiv.org/pdf/1411.4389.pdf

[5] 李飞飞及高徒Andrej Karpathy ★★★★★

Karpathy, Andrej, and Li Fei-Fei. “Deep visual-semantic alignments for generating image descriptions”. In arXiv preprint arXiv:1412.2306, 2014.

https://cs.stanford.edu/people/karpathy/cvpr2015.pdf

[6] 李飞飞及高徒Andrej Karpathy ★★★

Karpathy, Andrej, Armand Joulin, and Fei Fei F. Li. “Deep fragment embeddings for bidirectional image sentence mapping”. In Advances in neural information processing systems, 2014.

https://arxiv.org/pdf/1406.5679v1.pdf

[7] ★★★

Fang, Hao, et al. “From captions to visual concepts and back”. In arXiv preprint arXiv:1411.4952, 2014.

https://arxiv.org/pdf/1411.4952v3.pdf

[8] ★★★

Chen, Xinlei, and C. Lawrence Zitnick. “Learning a recurrent visual representation for image caption generation”. In arXiv preprint arXiv:1411.5654, 2014.

https://arxiv.org/pdf/1411.5654v1.pdf

[9]★★★

Mao, Junhua, et al. “Deep captioning with multimodal recurrent neural networks (m-rnn)”. In arXiv preprint arXiv:1412.6632, 2014.

https://arxiv.org/pdf/1412.6632v5.pdf

[10] ★★★★★

Xu, Kelvin, et al. “Show, attend and tell: Neural image caption generation with visual attention”. In arXiv preprint arXiv:1502.03044, 2015.

https://arxiv.org/pdf/1502.03044v3.pdf

机器翻译


本话题的部分里程碑论文列在“RNN / 序列到序列模型”话题下。

[1] ★★★

Luong, Minh-Thang, et al. “Addressing the rare word problem in neural machine translation.” arXiv preprint arXiv:1410.8206 (2014).

http://arxiv.org/pdf/1410.8206

[2] ★★★

Sennrich, et al. “Neural Machine Translation of Rare Words with Subword Units”. In arXiv preprint arXiv:1508.07909, 2015.

https://arxiv.org/pdf/1508.07909.pdf

[3]★★★

Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning. “Effective approaches to attention-based neural machine translation.” arXiv preprint arXiv:1508.04025 (2015).

http://arxiv.org/pdf/1508.04025

[4] ★★

Chung, et al. “A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation”. In arXiv preprint arXiv:1603.06147, 2016.

https://arxiv.org/pdf/1603.06147.pdf

[5] ★★★★★

Lee, et al. “Fully Character-Level Neural Machine Translation without Explicit Segmentation”. In arXiv preprint arXiv:1610.03017, 2016.

https://arxiv.org/pdf/1610.03017.pdf

[6] 里程碑 ★★★

Wu, Schuster, Chen, Le, et al. “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation”. In arXiv preprint arXiv:1609.08144v2, 2016.

https://arxiv.org/pdf/1609.08144v2.pdf

机器人


[1] ★★★

Koutník, Jan, et al. “Evolving large-scale neural networks for vision-based reinforcement learning.” Proceedings of the 15th annual conference on Genetic and evolutionary computation. ACM, 2013.

http://repository.supsi.ch/4550/1/koutnik2013gecco.pdf

[2] ★★★★★

Levine, Sergey, et al. “End-to-end training of deep visuomotor policies.” Journal of Machine Learning Research 17.39 (2016): 1-40.

http://www.jmlr.org/papers/volume17/15-522/15-522.pdf

[3] ★★★

Pinto, Lerrel, and Abhinav Gupta. “Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours.” arXiv preprint arXiv:1509.06825 (2015).

http://arxiv.org/pdf/1509.06825

[4] ★★★

Levine, Sergey, et al. “Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection.” arXiv preprint arXiv:1603.02199 (2016).

http://arxiv.org/pdf/1603.02199

[5] ★★★

Zhu, Yuke, et al. “Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning.” arXiv preprint arXiv:1609.05143 (2016).

https://arxiv.org/pdf/1609.05143

[6] ★★★

Yahya, Ali, et al. “Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search.” arXiv preprint arXiv:1610.00673 (2016).

https://arxiv.org/pdf/1610.00673

[7] ★★★

Gu, Shixiang, et al. “Deep Reinforcement Learning for Robotic Manipulation.” arXiv preprint arXiv:1610.00633 (2016).

https://arxiv.org/pdf/1610.00633

[8] ★★★

A Rusu, M Vecerik, Thomas Rothörl, N Heess, R Pascanu, R Hadsell.”Sim-to-Real Robot Learning from Pixels with Progressive Nets.” arXiv preprint arXiv:1610.04286 (2016).

https://arxiv.org/pdf/1610.04286.pdf

[9] ★★★

Mirowski, Piotr, et al. “Learning to navigate in complex environments.” arXiv preprint arXiv:1611.03673 (2016).

https://arxiv.org/pdf/1611.03673

艺术


[1] Google Deep Dream ★★★

Mordvintsev, Alexander; Olah, Christopher; Tyka, Mike (2015). “Inceptionism: Going Deeper into Neural Networks”. Google Research.

https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html

[2] 当前最为成功的艺术风格迁移方案,Prisma ★★★★★

Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A neural algorithm of artistic style.” arXiv preprint arXiv:1508.06576 (2015).

http://arxiv.org/pdf/1508.06576

[3] iGAN★★★

Zhu, Jun-Yan, et al. “Generative Visual Manipulation on the Natural Image Manifold.” European Conference on Computer Vision. Springer International Publishing, 2016.

https://arxiv.org/pdf/1609.03552

[4] Neural Doodle ★★★

Champandard, Alex J. “Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks.” arXiv preprint arXiv:1603.01768 (2016).

http://arxiv.org/pdf/1603.01768

[5] ★★★

Zhang, Richard, Phillip Isola, and Alexei A. Efros. “Colorful Image Colorization.” arXiv preprint arXiv:1603.08511 (2016).

http://arxiv.org/pdf/1603.08511

[6] 超分辨率,李飞飞 ★★★

Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. “Perceptual losses for real-time style transfer and super-resolution.” arXiv preprint arXiv:1603.08155 (2016).

https://arxiv.org/pdf/1603.08155.pdf

[7] ★★★

Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur. “A learned representation for artistic style.” arXiv preprint arXiv:1610.07629 (2016).

https://arxiv.org/pdf/1610.07629v1.pdf

[8] 基于空间位置、色彩信息与空间尺度的风格迁移 ★★★

Gatys, Leon and Ecker, et al.”Controlling Perceptual Factors in Neural Style Transfer.” arXiv preprint arXiv:1611.07865 (2016).

https://arxiv.org/pdf/1611.07865.pdf

[9] 纹理生成与风格迁移 ★★★

Ulyanov, Dmitry and Lebedev, Vadim, et al. “Texture Networks: Feed-forward Synthesis of Textures and Stylized Images.” arXiv preprint arXiv:1603.03417(2016).

http://arxiv.org/abs/1603.03417

目标分割


[1] ★★★★★

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation.” in CVPR, 2015.

https://arxiv.org/pdf/1411.4038v2.pdf

[2] ★★★★★

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. “Semantic image segmentation with deep convolutional nets and fully connected crfs.” In ICLR, 2015.

https://arxiv.org/pdf/1606.00915v1.pdf

[3] ★★★

Pinheiro, P.O., Collobert, R., Dollar, P. “Learning to segment object candidates.” In: NIPS. 2015.

https://arxiv.org/pdf/1506.06204v2.pdf

[4] ★★★

Dai, J., He, K., Sun, J. “Instance-aware semantic segmentation via multi-task network cascades.” in CVPR. 2016

https://arxiv.org/pdf/1512.04412v1.pdf

[5] ★★★

Dai, J., He, K., Sun, J. “Instance-sensitive Fully Convolutional Networks.” arXiv preprint arXiv:1603.08678 (2016).

https://arxiv.org/pdf/1603.08678v1.pdf

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