LoginSignup
91
82

More than 3 years have passed since last update.

@tmhssk1

ArxivのDeep Learning関連論文を被引用数順に1000本並べてみる

Last updated at Posted at 2019-05-27

Deep Learning関連の最新の論文をピックアップする方法ではなく、抄読会で取り上げるような目的で「のちの研究に大きな影響を与えた論文」を網羅的に探す方法が欲しかったため、論文を被引用数順に並べたリストの作成を試みた。

下記のコードで15000本の論文情報をArxivから取得してSemantic ScholarのAPIで被引用数を取得 (取得日は2019年5月27日)。 

import arxiv
import pandas as pd
import requests

result = arxiv.query(search_query="all:deep learning")
data = pd.DataFrame(columns = ["title","id",'arxiv_url','published'])

for i in range(len(result)):
  id = result[i]['id'].split("/")[-1].split("v")[0]
  title = result[i]['title']
  arxiv_url = result[i]['arxiv_url']
  published = result[i]['published']
  data_tmp = pd.DataFrame({"title":title,"id":id, "arxiv_url":arxiv_url, "published":published},index=[0])
  data = pd.concat([data,data_tmp]).reset_index(drop=True)

citation_num_list = []
for i in data["id"]:
  try:
    sem = requests.get("https://api.semanticscholar.org/v1/paper/arXiv:"+i).json()
    citation_num = len(sem["citations"])
  except:
    citation_num = 0
  citation_num_list.append(citation_num)

data["citation"] = citation_num_list

data = data.sort_values(by='citation', ascending=False)

data.to_csv("data.csv",index=False)

被引用数999以上は999+となって具体的な数値が得られなかったため結局手入力(30ちょいほどあった)。APIが不安定な挙動をするため、被引用数が取得できなかったレコードには0を割り当てており、そのために上位に出てこない論文があるので完璧なリストではない。

実際のところArxivのwebページでDeep Learningで検索すると16000ほど論文が引っかかってくるが、APIで得られたのは15000本であったため、ここでも抜けがあると思われる。そもそも検索単語が"Deep Learning"だけでいいのかという問題もある。

15000本の論文を得たにも関わらず、トップのResNetが被引用数16101なので、これも拾いきれていない論文があることを示唆する結果となった (Arxiv外の論文にも多々引用されているのかもしれないが)。

*34位の論文は、APIで被引用数999+の結果が得られたが、Arxivページに被引用数が表示されず、具体的な数字が不明。

**2019年5月29日修正：上位1000本の論文の結果をこちら(Googleスプレッドシート)に公開しました。1000件全てをこのページに貼り付けておりましたが、ページが重すぎるとのご意見を頂いてGoogleスプレッドシートのリンクに置き換えました。このページ内ではTop100のテーブルのみ表示しています。

Rank            Title Arxiv url Citation           Publish date          
1 Deep Residual Learning for Image Recognition http://arxiv.org/abs/1512.03385v1 16101 2015/12/10
2 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift http://arxiv.org/abs/1502.03167v3 7490 2015/2/11
3 Caffe: Convolutional Architecture for Fast Feature Embedding http://arxiv.org/abs/1408.5093v1 7352 2014/6/20
4 Sequence to Sequence Learning with Neural Networks http://arxiv.org/abs/1409.3215v3 5048 2014/9/10
5 TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems http://arxiv.org/abs/1603.04467v2 4343 2016/3/14
6 Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification http://arxiv.org/abs/1502.01852v1 3284 2015/2/6
7 Representation Learning: A Review and New Perspectives http://arxiv.org/abs/1206.5538v3 2915 2012/6/24
8 Deep Learning in Neural Networks: An Overview http://arxiv.org/abs/1404.7828v4 2854 2014/4/30
9 Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks http://arxiv.org/abs/1511.06434v2 2728 2015/11/19
10 TensorFlow: A system for large-scale machine learning http://arxiv.org/abs/1605.08695v2 2355 2016/5/27
11 FaceNet: A Unified Embedding for Face Recognition and Clustering http://arxiv.org/abs/1503.03832v3 2054 2015/3/12
12 OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks http://arxiv.org/abs/1312.6229v4 2037 2013/12/21
13 DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition http://arxiv.org/abs/1310.1531v1 2028 2013/10/6
14 Two-Stream Convolutional Networks for Action Recognition in Videos http://arxiv.org/abs/1406.2199v2 1866 2014/6/9
15 Intriguing properties of neural networks http://arxiv.org/abs/1312.6199v4 1710 2013/12/21
16 Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling http://arxiv.org/abs/1412.3555v1 1692 2014/12/11
17 DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs http://arxiv.org/abs/1606.00915v2 1679 2016/6/2
18 How transferable are features in deep neural networks? http://arxiv.org/abs/1411.1792v1 1670 2014/11/6
19 Playing Atari with Deep Reinforcement Learning http://arxiv.org/abs/1312.5602v1 1581 2013/12/19
20 Identity Mappings in Deep Residual Networks http://arxiv.org/abs/1603.05027v3 1494 2016/3/16
21 Multi-column Deep Neural Networks for Image Classification http://arxiv.org/abs/1202.2745v1 1485 2012/2/13
22 Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding http://arxiv.org/abs/1510.00149v5 1484 2015/10/1
23 Learning Spatiotemporal Features with 3D Convolutional Networks http://arxiv.org/abs/1412.0767v4 1412 2014/12/2
24 SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation http://arxiv.org/abs/1511.00561v3 1343 2015/11/2
25 Asynchronous Methods for Deep Reinforcement Learning http://arxiv.org/abs/1602.01783v2 1283 2016/2/4
26 Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs http://arxiv.org/abs/1412.7062v4 1281 2014/12/22
27 DeepWalk: Online Learning of Social Representations http://arxiv.org/abs/1403.6652v2 1249 2014/3/26
28 Network In Network http://arxiv.org/abs/1312.4400v3 1216 2013/12/16
29 The Cityscapes Dataset for Semantic Urban Scene Understanding http://arxiv.org/abs/1604.01685v2 1121 2016/4/6
30 Deep Learning Face Attributes in the Wild http://arxiv.org/abs/1411.7766v3 1112 2014/11/28
31 WaveNet: A Generative Model for Raw Audio http://arxiv.org/abs/1609.03499v2 1061 2016/9/12
32 Image Super-Resolution Using Deep Convolutional Networks http://arxiv.org/abs/1501.00092v3 1054 2014/12/31
33 Conditional Random Fields as Recurrent Neural Networks http://arxiv.org/abs/1502.03240v3 1028 2015/2/11
34 Distilling the Knowledge in a Neural Network http://arxiv.org/abs/1503.02531v1 *999 2015/3/9
35 Theano: new features and speed improvements http://arxiv.org/abs/1211.5590v1 973 2012/11/23
36 Feature Pyramid Networks for Object Detection http://arxiv.org/abs/1612.03144v2 872 2016/12/9
37 Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) http://arxiv.org/abs/1511.07289v5 844 2015/11/23
38 Character-level Convolutional Networks for Text Classification http://arxiv.org/abs/1509.01626v3 840 2015/9/4
39 Accurate Image Super-Resolution Using Very Deep Convolutional Networks http://arxiv.org/abs/1511.04587v2 825 2015/11/14
40 End-to-End Training of Deep Visuomotor Policies http://arxiv.org/abs/1504.00702v5 809 2015/4/2
41 Wide Residual Networks http://arxiv.org/abs/1605.07146v4 804 2016/5/23
42 Learning Deep Features for Discriminative Localization http://arxiv.org/abs/1512.04150v1 800 2015/12/14
43 Teaching Machines to Read and Comprehend http://arxiv.org/abs/1506.03340v3 789 2015/6/10
44 Deep Learning Face Representation by Joint Identification-Verification http://arxiv.org/abs/1406.4773v1 772 2014/6/18
45 DeepPose: Human Pose Estimation via Deep Neural Networks http://arxiv.org/abs/1312.4659v3 768 2013/12/17
46 DRAW: A Recurrent Neural Network For Image Generation http://arxiv.org/abs/1502.04623v2 758 2015/2/16
47 Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning http://arxiv.org/abs/1506.02142v6 716 2015/6/6
48 Semi-Supervised Learning with Deep Generative Models http://arxiv.org/abs/1406.5298v2 704 2014/6/20
49 Understanding deep learning requires rethinking generalization http://arxiv.org/abs/1611.03530v2 700 2016/11/10
50 Generative Adversarial Text to Image Synthesis http://arxiv.org/abs/1605.05396v2 697 2016/5/17
51 3D ShapeNets: A Deep Representation for Volumetric Shapes http://arxiv.org/abs/1406.5670v3 693 2014/6/22
52 Xception: Deep Learning with Depthwise Separable Convolutions http://arxiv.org/abs/1610.02357v3 688 2016/10/7
53 PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation http://arxiv.org/abs/1612.00593v2 676 2016/12/2
54 Deep Reinforcement Learning with Double Q-learning http://arxiv.org/abs/1509.06461v3 674 2015/9/22
55 Deep Speech 2: End-to-End Speech Recognition in English and Mandarin http://arxiv.org/abs/1512.02595v1 659 2015/12/8
56 Domain-Adversarial Training of Neural Networks http://arxiv.org/abs/1505.07818v4 654 2015/5/28
57 BinaryConnect: Training Deep Neural Networks with binary weights during propagations http://arxiv.org/abs/1511.00363v3 651 2015/11/2
58 Learning Transferable Features with Deep Adaptation Networks http://arxiv.org/abs/1502.02791v2 607 2015/2/10
59 Unsupervised Domain Adaptation by Backpropagation http://arxiv.org/abs/1409.7495v2 602 2014/9/26
60 MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems http://arxiv.org/abs/1512.01274v1 601 2015/12/3
61 DeepFool: a simple and accurate method to fool deep neural networks http://arxiv.org/abs/1511.04599v3 591 2015/11/14
62 Wasserstein GAN http://arxiv.org/abs/1701.07875v3 588 2017/1/26
63 Pixel Recurrent Neural Networks http://arxiv.org/abs/1601.06759v3 588 2016/1/25
64 Deep contextualized word representations http://arxiv.org/abs/1802.05365v2 587 2018/2/15
65 Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering http://arxiv.org/abs/1606.09375v3 579 2016/6/30
66 Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning http://arxiv.org/abs/1602.03409v1 567 2016/2/10
67 Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks http://arxiv.org/abs/1511.04508v2 564 2015/11/14
68 Practical recommendations for gradient-based training of deep architectures http://arxiv.org/abs/1206.5533v2 560 2012/6/24
69 Temporal Segment Networks: Towards Good Practices for Deep Action Recognition http://arxiv.org/abs/1608.00859v1 556 2016/8/2
70 Deep multi-scale video prediction beyond mean square error http://arxiv.org/abs/1511.05440v6 554 2015/11/17
71 Convolutional Sequence to Sequence Learning http://arxiv.org/abs/1705.03122v3 552 2017/5/8
72 Deep Speech: Scaling up end-to-end speech recognition http://arxiv.org/abs/1412.5567v2 538 2014/12/17
73 Towards Deep Learning Models Resistant to Adversarial Attacks http://arxiv.org/abs/1706.06083v3 534 2017/6/19
74 Bag of Tricks for Efficient Text Classification http://arxiv.org/abs/1607.01759v3 532 2016/7/6
75 Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising http://arxiv.org/abs/1608.03981v1 530 2016/8/13
76 The Limitations of Deep Learning in Adversarial Settings http://arxiv.org/abs/1511.07528v1 530 2015/11/24
77 Matching Networks for One Shot Learning http://arxiv.org/abs/1606.04080v2 529 2016/6/13
78 Learning Face Representation from Scratch http://arxiv.org/abs/1411.7923v1 527 2014/11/28
79 A Survey on Deep Learning in Medical Image Analysis http://arxiv.org/abs/1702.05747v2 520 2017/2/19
80 Understanding Neural Networks Through Deep Visualization http://arxiv.org/abs/1506.06579v1 520 2015/6/22
81 Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network http://arxiv.org/abs/1609.05158v2 516 2016/9/16
82 Deep Learning with Limited Numerical Precision http://arxiv.org/abs/1502.02551v1 513 2015/2/9
83 cuDNN: Efficient Primitives for Deep Learning http://arxiv.org/abs/1410.0759v3 509 2014/10/3
84 Deeply-Supervised Nets http://arxiv.org/abs/1409.5185v2 509 2014/9/18
85 Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks http://arxiv.org/abs/1604.02878v1 503 2016/4/11
86 From Captions to Visual Concepts and Back http://arxiv.org/abs/1411.4952v3 501 2014/11/18
87 Do Deep Nets Really Need to be Deep? http://arxiv.org/abs/1312.6184v7 496 2013/12/21
88 Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors http://arxiv.org/abs/1505.04868v1 494 2015/5/19
89 A Theoretically Grounded Application of Dropout in Recurrent Neural Networks http://arxiv.org/abs/1512.05287v5 487 2015/12/16
90 FitNets: Hints for Thin Deep Nets http://arxiv.org/abs/1412.6550v4 483 2014/12/19
91 Spectral Networks and Locally Connected Networks on Graphs http://arxiv.org/abs/1312.6203v3 477 2013/12/21
92 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks http://arxiv.org/abs/1703.03400v3 467 2017/3/9
93 Identifying and attacking the saddle point problem in high-dimensional non-convex optimization http://arxiv.org/abs/1406.2572v1 459 2014/6/10
94 Exact solutions to the nonlinear dynamics of learning in deep linear neural networks http://arxiv.org/abs/1312.6120v3 455 2013/12/20
95 Prioritized Experience Replay http://arxiv.org/abs/1511.05952v4 452 2015/11/18
96 Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) http://arxiv.org/abs/1412.6632v5 446 2014/12/20
97 Training Very Deep Networks http://arxiv.org/abs/1507.06228v2 441 2015/7/22
98 Dueling Network Architectures for Deep Reinforcement Learning http://arxiv.org/abs/1511.06581v3 441 2015/11/20
99 Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models http://arxiv.org/abs/1411.2539v1 431 2014/11/10
100 Learning Fine-grained Image Similarity with Deep Ranking http://arxiv.org/abs/1404.4661v1 426 2014/4/17

Top1000の結果はこちら(Googleスプレッドシート)にあります。

Markdownのテーブルの幅調整むずかしい・・・

91
82
2

Register as a new user and use Qiita more conveniently

  1. You get articles that match your needs
  2. You can efficiently read back useful information
  3. You can use dark theme
What you can do with signing up
Sign upLogin
91
82