126篇殿堂级深度学习论文分类整理 从入门到应用(下)

AI 研习社:本文接“126篇殿堂级深度学习论文分类整理 从入门到应用(上)”,是该整理的下半部分,即应用篇;按照各应用领域对论文进行分类。

3 应用

3.1 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) [pdf] ★★★★

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

█[2] Mikolov, et al. "Distributed representations of words and phrases and their compositionality." ANIPS(2013): 3111-3119 [pdf] (word2vec) ★★★

地址: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) [pdf] ★★★

地址: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) [pdf] ★★★★

地址:https://arxiv.org/abs/1506.07285

█[5] Yoon Kim, et al. "Character-Aware Neural Language Models." NIPS(2015) arXiv preprint arXiv:1508.06615(2015) [pdf] ★★★

地址:https://arxiv.org/abs/1508.06615

█[6] Jason Weston, et al. "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks." arXiv preprint arXiv:1502.05698(2015) [pdf] (bAbI tasks) ★★★

地址:https://arxiv.org/abs/1502.05698

█[7] Karl Moritz Hermann, et al. "Teaching Machines to Read and Comprehend." arXiv preprint arXiv:1506.03340(2015) [pdf](CNN/每日邮报完形填空风格的问题) ★★

地址:https://arxiv.org/abs/1506.03340

█[8] Alexis Conneau, et al. "Very Deep Convolutional Networks for Natural Language Processing." arXiv preprint arXiv:1606.01781(2016) [pdf] (文本分类的前沿技术) ★★★

地址:https://arxiv.org/abs/1606.01781

█[9] Armand Joulin, et al. "Bag of Tricks for Efficient Text Classification." arXiv preprint arXiv:1607.01759(2016) [pdf] (比前沿技术稍落后, 但快很多) ★★★

地址:https://arxiv.org/abs/1607.01759

3.2 物体检测

█[1] Szegedy, Christian, Alexander Toshev, and Dumitru Erhan. "Deep neural networks for object detection." Advances in Neural Information Processing Systems. 2013. [pdf] ★★★

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

█[2] 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. [pdf] (RCNN) ★★★★★

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

█[3] He, Kaiming, et al. "Spatial pyramid pooling in deep convolutional networks for visual recognition." European Conference on Computer Vision. Springer International Publishing, 2014. [pdf] (SPPNet) ★★★★

地址:http://arxiv.org/pdf/1406.4729

█[4] Girshick, Ross. "Fast r-cnn." Proceedings of the IEEE International Conference on Computer Vision. 2015. [pdf] ★★★★

地址: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. [pdf] ★★★★

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

█[6] Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1506.02640 (2015). [pdf] (YOLO,杰出研究,非常具有使用价值) ★★★★★

地址:http://homes.cs.washington.edu/~ali/papers/YOLO.pdf

█[7] Liu, Wei, et al. "SSD: Single Shot MultiBox Detector." arXiv preprint arXiv:1512.02325 (2015). [pdf] ★★★

地址: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). [pdf] ★★★★

地址:https://arxiv.org/abs/1605.06409

3.3 视觉追踪

█[1] Wang, Naiyan, and Dit-Yan Yeung. "Learning a deep compact image representation for visual tracking." Advances in neural information processing systems. 2013. [pdf] (第一篇使用深度学习做视觉追踪的论文,DLT Tracker) ★★★

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

█[2] Wang, Naiyan, et al. "Transferring rich feature hierarchies for robust visual tracking." arXiv preprint arXiv:1501.04587 (2015). [pdf] (SO-DLT) ★★★★

地址:http://arxiv.org/pdf/1501.04587

█[3] Wang, Lijun, et al. "Visual tracking with fully convolutional networks." Proceedings of the IEEE International Conference on Computer Vision. 2015. [pdf] (FCNT) ★★★★

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

█[4] Held, David, Sebastian Thrun, and Silvio Savarese. "Learning to Track at 100 FPS with Deep Regression Networks." arXiv preprint arXiv:1604.01802 (2016). [pdf] (GOTURN,在深度学习方法里算是非常快的,但仍比非深度学习方法慢很多) ★★★★

地址:http://arxiv.org/pdf/1604.01802

█[5] Bertinetto, Luca, et al. "Fully-Convolutional Siamese Networks for Object Tracking." arXiv preprint arXiv:1606.09549 (2016). [pdf] (SiameseFC,实时物体追踪领域的最新前沿技术) ★★★★

地址:https://arxiv.org/pdf/1606.09549

█[6] Martin Danelljan, Andreas Robinson, Fahad Khan, Michael Felsberg. "Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking." ECCV (2016) [pdf] (C-COT) ★★★★

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

█[7] Nam, Hyeonseob, Mooyeol Baek, and Bohyung Han. "Modeling and Propagating CNNs in a Tree Structure for Visual Tracking." arXiv preprint arXiv:1608.07242 (2016). [pdf] (VOT2016 获奖论文,TCNN) ★★★★

地址:https://arxiv.org/pdf/1608.07242

3.4 图像标注

█[1] Farhadi,Ali,etal. "Every picture tells a story: Generating sentences from images". In Computer VisionECCV 2010. Springer Berlin Heidelberg:15-29, 2010. [pdf] ★★★

地址:https://www.cs.cmu.edu/~afarhadi/papers/sentence.pdf

█[2] Kulkarni, Girish, et al. "Baby talk: Understanding and generating image descriptions". In Proceedings of the 24th CVPR, 2011. [pdf] ★★★★

地址: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. [pdf] ★★★

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

█[4] Donahue, Jeff, et al. "Long-term recurrent convolutional networks for visual recognition and description". In arXiv preprint arXiv:1411.4389 ,2014. [pdf]

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

█[5] Karpathy, Andrej, and Li Fei-Fei. "Deep visual-semantic alignments for generating image descriptions". In arXiv preprint arXiv:1412.2306, 2014. [pdf] ★★★★★

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

█[6] 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. [pdf] ★★★★

地址: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. [pdf] ★★★★★

地址: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. [pdf] ★★★★

地址: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. [pdf] ★★★

地址: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. [pdf] ★★★★★

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

3.5 机器翻译

部分里程碑研究被列入 RNN / Seq-to-Seq 版块。

█[1] Luong, Minh-Thang, et al. "Addressing the rare word problem in neural machine translation." arXiv preprint arXiv:1410.8206 (2014). [pdf] ★★★★

地址: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. [pdf] ★★★

地址: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). [pdf] ★★★★

地址: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. [pdf] ★★

地址: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. [pdf] ★★★★★

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

█[6] 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. [pdf] (Milestone) ★★★★

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

3.6 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. [pdf] ★★★

地址: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. [pdf] ★★★★★

地址: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). [pdf] ★★★

地址: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). [pdf] ★★★★

地址: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). [pdf] ★★★★

地址: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). [pdf] ★★★★

地址:https://arxiv.org/pdf/1610.00673

█[7] Gu, Shixiang, et al. "Deep Reinforcement Learning for Robotic Manipulation." arXiv preprint arXiv:1610.00633 (2016). [pdf] ★★★★

地址: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). [pdf] ★★★★

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

█[9] Mirowski, Piotr, et al. "Learning to navigate in complex environments." arXiv preprint arXiv:1611.03673 (2016). [pdf] ★★★★

地址:https://arxiv.org/pdf/1611.03673

3.7 艺术

█[1] Mordvintsev, Alexander; Olah, Christopher; Tyka, Mike (2015). "Inceptionism: Going Deeper into Neural Networks". Google Research. [html] (Deep Dream) ★★★★

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

█[2] Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015). [pdf] (杰出研究,迄今最成功的方法) ★★★★★

地址:http://arxiv.org/pdf/1508.06576

█[3] Zhu, Jun-Yan, et al. "Generative Visual Manipulation on the Natural Image Manifold." European Conference on Computer Vision. Springer International Publishing, 2016. [pdf] (iGAN) ★★★★

地址:https://arxiv.org/pdf/1609.03552

█[4] Champandard, Alex J. "Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks." arXiv preprint arXiv:1603.01768 (2016). [pdf] (Neural Doodle) ★★★★

地址:http://arxiv.org/pdf/1603.01768

█[5] Zhang, Richard, Phillip Isola, and Alexei A. Efros. "Colorful Image Colorization." arXiv preprint arXiv:1603.08511 (2016). [pdf] ★★★★

地址:http://arxiv.org/pdf/1603.08511

█[6] Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." arXiv preprint arXiv:1603.08155 (2016). [pdf] ★★★★

地址: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). [pdf] ★★★★

地址:https://arxiv.org/pdf/1610.00633

█[8] Gatys, Leon and Ecker, et al."Controlling Perceptual Factors in Neural Style Transfer." arXiv preprint arXiv:1611.07865 (2016). [pdf] (control style transfer over spatial location,colour information and across spatial scale) ★★★★

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

█[9] Ulyanov, Dmitry and Lebedev, Vadim, et al. "Texture Networks: Feed-forward Synthesis of Textures and Stylized Images." arXiv preprint arXiv:1603.03417(2016). [pdf] (纹理生成和风格变化) ★★★★

地址:https://arxiv.org/pdf/1611.03673

3.8 目标分割 Object Segmentation

█[1] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation.” in CVPR, 2015. [pdf] ★★★★★

地址: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. [pdf] ★★★★★

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

█[3] Pinheiro, P.O., Collobert, R., Dollar, P. "Learning to segment object candidates." In: NIPS. 2015. [pdf] ★★★★

地址: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 [pdf] ★★★

地址: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). [pdf] ★★★

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

原文地址:https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap

原文发布于微信公众号 - AI研习社(okweiwu)

原文发表时间:2017-03-03

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