超干货！一位博士生80篇机器学习相关论文及笔记下载

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  新智元报道  

来源：GitHub

编辑：元子

【新智元导读】博士生论文阅读笔记，涵盖深度学习、CV和NLP。作为一名博士生，作者读了很多论文，有时会用一个简单的模板进行简短总结，以更好地理解并更清楚论文的贡献。作者将笔记存放在GitHub里，供大家参考。来新智元AI朋友圈 和AI大咖们一起讨论吧。

好像很多人都觉得读论文做笔记是一件非常正确、重要、且必要的事情。 你可能没少看有关《为什么习惯记笔记的人更容易成功》《学霸的笔记都是这么做的，难怪他们的考试分数那么高！ 》等文章。

论文笔记的作用不仅仅是起到一种心理安慰，更重要的是划出重点、标记出知识盲区，便于后续温习。 德国心理学家艾宾浩斯研究发现，人类在学习后就会开始遗忘，遗忘的程度是不均匀的，刚开始记忆下降比例很高，后面会越来越少。

根据实验结果发现刚记住的时候是100%，过了20分钟记忆程度只有58.2%，2天和6天后分别只能记得27.8%和25.4%。

如果只是单纯的看论文，很难将里面的知识点据为己有，这个时候就需要笔记来帮忙来延缓知识的流失速度了。

我们经常看到网上动不动就最全XX论文全集，上来就是几十篇论文。 人类都有囤积物资的欲求，囤积论文也差不多。 我们往往觉得，我手中的论文数量越多，带给我的安全感就越大、我能收获的知识就越多。 虽然理智告诉我们： 这样想法是错误的！ 然而欲望却拖着我们像个过冬的松鼠一样，不断的收集松果。

但如果硬是强迫自己看一篇论文就必须要做多少多少笔记，也不太现实。 而且如果一开始没有培养期做笔记习惯的话，很可能一开始并没有get到做笔记的精髓，导致事无巨细全都记笔记。 这个时候，看看别人做的笔记，尤其是看看学霸做的笔记，也是一个非常不错、极具实操性的方法。

今天新智元为大家带来一位博士生的论文笔记。 这位学霸真的是有很认真的看了不少论文，为了便于检索，他在看过的ML相关的论文进行了注释和简短摘要，并且将这些摘要按主题分类。

他将论文和笔记都放在了GitHub上，非常方便进行对照。 以下就是论文和笔记的列表，大家可以根据需要下载阅读。

Self-Supervised Learning

论文标题： Selfie: Self-supervised Pretraining for Image Embedding (2019)

论文链接：

https://arxiv.org/abs/1906.02940

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/76_selfie_pretraining_for_img_embeddings.pdf

论文标题： Self-Supervised Representation Learning by Rotation Feature Decoupling (2019)

论文链接：

https://github.com/philiptheother/FeatureDecoupling

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/73_SSL_by_rotation_decoupling.pdf

论文标题： Revisiting Self-Supervised Visual Representation Learning (2019)

论文链接：

https://arxiv.org/abs/1901.09005

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/72_revisiting_SSL.pdf

论文标题： AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data (2019)

论文链接：

https://arxiv.org/abs/1901.04596

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/74_AFT_vs_AED.pdf

论文标题： Boosting Self-Supervised Learning via Knowledge Transfer (2018)

论文链接：

https://arxiv.org/abs/1805.00385

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/67_boosting_self_super_via_trsf_learning.pdf

论文标题： Self-Supervised Feature Learning by Learning to Spot Artifacts (2018)

论文链接：

https://arxiv.org/abs/1806.05024

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/69_SSL_by_learn_to_spot_artifacts.pdf

论文标题： Unsupervised Representation Learning by Predicting Image Rotations (2018)

论文链接：

https://arxiv.org/abs/1803.07728

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/68_unsup_img_rep_learn_by_rot_predic.pdf

论文标题： Cross Pixel Optical-Flow Similarity for Self-Supervised Learning (2018)

论文链接：

https://arxiv.org/abs/1807.05636

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/75_cross_pixel_optical_flow.pdf

论文标题： Multi-task Self-Supervised Visual Learning (2017)

论文链接：

https://arxiv.org/abs/1708.07860

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/64_multi_task_self_supervised.pdf

论文标题： Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction (2017)

论文链接：

https://arxiv.org/abs/1611.09842

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/65_split_brain_autoencoders.pdf

论文标题： Colorization as a Proxy Task for Visual Understanding (2017)

论文链接：

https://arxiv.org/abs/1703.04044

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/66_colorization_as_a_proxy_for_viz_under.pdf

论文标题： Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles (2017)

论文链接：

https://arxiv.org/abs/1603.09246

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/63_solving_jigsaw_puzzles.pdf

论文标题： Unsupervised Visual Representation Learning by Context Prediction (2016)

论文链接：

https://arxiv.org/abs/1505.05192

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/62_unsupervised_learning_with_context_prediction.pdf

论文标题： Colorful image colorization (2016)

论文链接：

https://richzhang.github.io/colorization/

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/59_colorful_colorization.pdf

论文标题： Learning visual groups from co-occurrences in space and time (2015)

论文链接：

https://arxiv.org/abs/1511.06811

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/61_visual_groups_from_co_occurrences.pdf

论文标题： Discriminative unsupervised feature learning with exemplar convolutional neural networks (2015)

论文链接：

https://arxiv.org/abs/1406.6909

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/60_exemplar_CNNs.pdf

Semi-Supervised Learning

论文标题： Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning (2019)

论文链接：

https://arxiv.org/abs/1909.01804

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/79_dual_student.pdf

论文标题： S4L: Self-Supervised Semi-Supervised Learning (2019)

论文链接：

https://arxiv.org/abs/1905.03670

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/83_S4L.pdf

论文标题： Semi-Supervised Learning by Augmented Distribution Alignment (2019)

论文链接：

https://arxiv.org/abs/1905.08171

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/80_SSL_aug_dist_align.pdf

论文标题： MixMatch: A Holistic Approach toSemi-Supervised Learning (2019)

论文链接：

https://arxiv.org/abs/1905.02249

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/45_mixmatch.pdf

论文标题： Unsupervised Data Augmentation (2019)

论文链接：

https://arxiv.org/abs/1904.12848

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/39_unsupervised_data_aug.pdf

论文标题： Interpolation Consistency Training forSemi-Supervised Learning (2019)

论文链接：

https://arxiv.org/abs/1903.03825

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/44_interpolation_consistency_tranining.pdf

论文标题： Deep Co-Training for Semi-Supervised Image Recognition (2018)

论文链接：

https://arxiv.org/abs/1803.05984

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/46_deep_co_training_img_rec.pdf

论文标题： Unifying semi-supervised and robust learning by mixup (2019)

论文链接：

https://openreview.net/forum?id=r1gp1jRN_4

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/42_mixmixup.pdf

论文标题： Realistic Evaluation of Deep Semi-Supervised Learning Algorithms (2018)

论文链接：

https://arxiv.org/abs/1804.09170

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/37_realistic_eval_of_deep_ss.pdf

论文标题： Semi-Supervised Sequence Modeling with Cross-View Training (2018)

论文链接：

https://arxiv.org/abs/1809.08370

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/38_cross_view_semi_supervised.pdf

论文标题： Virtual Adversarial Training:A Regularization Method for Supervised andSemi-Supervised Learning (2017)

论文链接：

https://arxiv.org/abs/1704.03976

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/40_virtual_adversarial_training.pdf

论文标题： Mean teachers are better role models (2017)

论文链接：

https://arxiv.org/abs/1703.01780

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/56_mean_teachers.pdf

论文标题： Temporal Ensembling for Semi-Supervised Learning (2017)

论文链接：

https://arxiv.org/abs/1610.02242

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/55_temporal-ensambling.pdf

论文标题： Semi-Supervised Learning with Ladder Networks (2015)

论文链接：

https://arxiv.org/abs/1507.02672

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/33_ladder_nets.pdf

Unsupervised Learning

论文标题： Invariant Information Clustering for Unsupervised Image Classification and Segmentation (2019)

论文链接：

https://arxiv.org/abs/1807.06653

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/78_IIC.pdf

论文标题： Deep Clustering for Unsupervised Learning of Visual Feature (2018)

论文链接：

https://arxiv.org/abs/1807.05520

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/70_deep_clustering_for_un_visual_features.pdf

Semantic Segmentation

论文标题： DeepLabv3+: Encoder-Decoder with Atrous Separable Convolution (2018)

论文链接：

https://arxiv.org/abs/1802.02611

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/26_deeplabv3+.pdf

论文标题： Large Kernel Matter, Improve Semantic Segmentation by Global Convolutional Network (2017)

论文链接：

https://arxiv.org/abs/1703.02719

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/28_large_kernel_maters.pdf

论文标题： Understanding Convolution for Semantic Segmentation (2018)

论文链接：

https://arxiv.org/abs/1702.08502

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/29_understanding_conv_for_sem_seg.pdf

论文标题： Rethinking Atrous Convolution for Semantic Image Segmentation (2017)

论文链接：

https://arxiv.org/abs/1706.05587

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/25_deeplab_v3.pdf

论文标题： RefineNet: Multi-path refinement networks for high-resolution semantic segmentation (2017)

论文链接：

https://arxiv.org/abs/1611.06612

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/31_refinenet.pdf

论文标题： Pyramid Scene Parsing Network (2017)

论文链接：

http://jiaya.me/papers/PSPNet_cvpr17.pdf

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/22_pspnet.pdf

论文标题： SegNet: A Deep ConvolutionalEncoder-Decoder Architecture for ImageSegmentation (2016)

论文链接：

https://arxiv.org/pdf/1511.00561

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/21_segnet.pdf

论文标题： ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation (2016)

论文链接：

https://arxiv.org/abs/1606.02147

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/27_enet.pdf

论文标题： Attention to Scale: Scale-aware Semantic Image Segmentation (2016)

论文链接：

https://arxiv.org/abs/1511.03339

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/30_atttention_to_scale.pdf

论文标题： Deeplab: semantic image segmentation with DCNN, atrous convs and CRFs (2016)

论文链接：

https://arxiv.org/abs/1606.00915

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/23_deeplab_v2.pdf

论文标题： U-Net: Convolutional Networks for Biomedical Image Segmentation (2015)

论文链接：

https://arxiv.org/abs/1505.04597

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/20_Unet.pdf

论文标题： Fully Convolutional Networks for Semantic Segmentation (2015)

论文链接：

https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/19_FCN.pdf

论文标题： Hypercolumns for object segmentation and fine-grained localization (2015)

论文链接：

http://home.bharathh.info/pubs/pdfs/BharathCVPR2015.pdf

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/24_hypercolumns.pdf

Weakly

论文标题： Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation (2019)

论文链接：

http://arxiv.org/abs/1904.11693

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/54_boxe_driven_weakly_segmentation.pdf

论文标题： FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference (2019)

论文链接：

https://arxiv.org/abs/1902.10421

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/49_ficklenet.pdf

论文标题： Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (2018)

论文链接：

http://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_Weakly-Supervised_Semantic_Segmentation_CVPR_2018_paper.pdf

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/53_deep_seeded_region_growing.pdf

论文标题： Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation (2018)

论文链接：

https://arxiv.org/abs/1803.10464

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/81_affinity_for_ws_segmentation.pdf

论文标题： Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach (2018)

论文链接：

https://arxiv.org/abs/1703.08448

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/51_object_region_manning_for_sem_seg.pdf

论文标题： Revisiting Dilated Convolution: A Simple Approach for Weakly

论文标题： and Semi

论文标题： Supervised Semantic Segmentation (2018)

论文链接：

https://arxiv.org/abs/1805.04574

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/52_dilates_convolution_semi_super_segmentation.pdf

论文标题： Tell Me Where to Look: Guided Attention Inference Network (2018)

论文链接：

https://arxiv.org/abs/1802.10171

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/50_tell_me_where_to_look.pdf

论文标题： Semi Supervised Semantic Segmentation Using Generative Adversarial Network (2017)

论文链接：

https://arxiv.org/abs/1703.09695

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/82_ss_segmentation_gans.pdf

论文标题： Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation (2015)

论文链接：

https://arxiv.org/abs/1506.04924

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/47_decoupled_nn_for_segmentation.pdf

论文标题： Weakly

论文标题： and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation (2015)

论文链接：

https://arxiv.org/abs/1502.02734

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/48_weakly_and_ss_for_segmentation.pdf

Information Retrieval

论文标题： VSE++: Improving Visual-Semantic Embeddings with Hard Negatives (2018)

论文链接：

https://arxiv.org/abs/1707.05612

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/77_vse++.pdf

Visual Explanation & Attention

论文标题： Attention Branch Network: Learning of Attention Mechanism for Visual Explanation (2019)

论文链接：

https://arxiv.org/abs/1812.10025

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/57_attention_branch_netwrok.pdf

论文标题： Attention-based Dropout Layer for Weakly Supervised Object Localization (2019)

论文链接：

http://openaccess.thecvf.com/content_CVPR_2019/papers/Choe_Attention-Based_Dropout_Layer_for_Weakly_Supervised_Object_Localization_CVPR_2019_paper.pdf

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/58_attention_based_dropout.pdf

论文标题： Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer (2016)

论文链接：

https://arxiv.org/abs/1612.03928

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/71_attention_transfer.pdf

Graph neural network & Graph embeddings

论文标题： Pixels to Graphs by Associative Embedding (2017)

论文链接：

https://arxiv.org/abs/1706.07365

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/36_pixels_to_graphs.pdf

论文标题： Associative Embedding: End-to-End Learning forJoint Detection and Grouping (2017)

论文链接：

https://arxiv.org/abs/1611.05424

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/35_associative_emb.pdf

论文标题： Interaction Networks for Learning about Objects , Relations and Physics (2016)

论文链接：

https://arxiv.org/abs/1612.00222

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/18_interaction_nets.pdf

论文标题： DeepWalk: Online Learning of Social Representation (2014)

论文链接：

http://www.perozzi.net/publications/14_kdd_deepwalk.pdf

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/deep_walk.pdf

论文标题： The graph neural network model (2009)

论文链接：

https://persagen.com/files/misc/scarselli2009graph.pdf

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/graph_neural_nets.pdf

Regularization

论文标题： Manifold Mixup: Better Representations by Interpolating Hidden States (2018)

论文链接：

https://arxiv.org/abs/1806.05236

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/43_manifold_mixup.pdf

Deep learning Methods & Models

论文标题： AutoAugment (2018)

论文链接：

https://arxiv.org/abs/1805.09501

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/41_autoaugment.pdf

论文标题： Stacked Hourgloass (2017)

论文链接：

http://ismir2018.ircam.fr/doc/pdfs/138_Paper.pdf

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/34_stacked_hourglass.pdf

Document analysis and segmentation

论文标题： dhSegment: A generic deep-learning approach for document segmentation (2018)

论文链接：

https://arxiv.org/abs/1804.10371

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/dhSegement.pdf

论文标题： Learning to extract semantic structure from documents using multimodal fully convolutional neural networks (2017)

论文链接：

https://arxiv.org/abs/1706.02337

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/learning_to_extract.pdf

论文标题： Page Segmentation for Historical Handwritten Document Images Using Conditional Random Fields (2016)

论文链接：

https://www.researchgate.net/publication/312486501_Page_Segmentation_for_Historical_Handwritten_Document_Images_Using_Conditional_Random_Fields

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/seg_with_CRFs.pdf

论文标题： ICDAR 2015 competition on text line detection in historical documents (2015)

论文链接：

http://ieeexplore.ieee.org/abstract/document/7333945/

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/ICDAR2015.pdf

论文标题： Handwritten text line segmentation using Fully Convolutional Network (2017)

论文链接：

https://ieeexplore.ieee.org/document/8270267/

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/handwritten_text_seg_FCN.pdf

论文标题： Deep Neural Networks for Large Vocabulary Handwritten Text Recognition (2015)

论文链接：

https://tel.archives-ouvertes.fr/tel-01249405/document

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/andwriten_text_recognition.pdf

论文标题： Page Segmentation of Historical Document Images with Convolutional Autoencoders (2015)

论文链接：

https://ieeexplore.ieee.org/abstract/document/7333914/

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/segmentation_with_CAE.pdf

论文标题： A typed and handwritten text block segmentation system for heterogeneous and complex documents (2012)

论文链接：

https://www.researchgate.net/publication/275518176_A_Typed_and_Handwritten_Text_Block_Segmentation_System_for_Heterogeneous_and_Complex_Documents

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/a_typed_block_seg.pdf

论文标题： Document layout analysis, Classical approaches (1992:2001)

论文链接：

https://pdfs.semanticscholar.org/5392/90b571b918da959fabaae7f605bb07850518.pdf

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/old_classical_approaches.pdf

论文标题： Page Segmentation for Historical Document Images Based on Superpixel Classification with Unsupervised Feature Learning (2016)

论文链接：

https://ieeexplore.ieee.org/document/7490134

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/seg_with_superpixels.pdf

论文标题： Paragraph text segmentation into lines with Recurrent Neural Networks (2015)

论文链接：

http://ieeexplore.ieee.org/abstract/document/7333803/

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/textlines_srg_with_RNNs.pdf

论文标题： A comprehensive survey of mostly textual document segmentation algorithms since 2008 (2017 )

论文链接：

https://hal.archives-ouvertes.fr/hal-01388088/document

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/survey_doc_segmentation.pdf

论文标题： Convolutional Neural Networks for Page Segmentation of Historical Document Images (2017)

论文链接：

https://arxiv.org/abs/1704.01474

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/CNNs_chen.pdf

论文标题： ICDAR2009 Page Segmentation Competition (2009)

论文链接：

https://ieeexplore.ieee.org/document/5277763

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/ICDAR2009.pdf

论文标题： Amethod for combining complementary techniques for document image segmentation (2009)

论文链接：

https://www.researchgate.net/publication/220600948_A_method_for_combining_complementary_techniques_for_document_image_segmentation

笔记链接：

https://github.com/yassouali/ML_paper_notes/blob/master/notes/a_method_for_combining_complementary_techniques.pdf

完整列表：

https://github.com/yassouali/ML_paper_notes