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・・・