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Science NestCategoriesComputer Science
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  • WSD algorithm based on a new method of vector-word contexts proximity calculation via epsilon-filtration

    Alexander Kirillov, Andrew Krizhanovsky, Natalia Krizhanovsky , Proceedings of the Karelian Research Centre of the Russian Academy of Sciences ,  2018
    PHP

    Free

  • Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection

    Sebastian Riedel, Sudhanshu Kasewa, Pontus Stenetorp , EMNLP 2018 10 ,  2018
    Python

    Free

  • WriterForcing: Generating more interesting story endings

    Prakhar Gupta, Alan W Black, Mukul Bhutani, Vinayshekhar Bannihatti Kumar , WS 2019 8 ,  2019
    Python

    Free

  • Worst-case Optimal Submodular Extensions for Marginal Estimation

    Pankaj Pansari, Chris Russell, M. Pawan Kumar , International Conference on Artificial Intelligence and Statistics, AISTATS 2018 ,  2018
    C++

    Free

  • WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans

    Greg J Stephens, Tosif Ahamed, Laetitia Hebert, Antonio C Costa, Liam O'Shaughnessy , bioRxiv 2020 7 ,  2020
    Python

    Free

  • World Models

    David Ha, Jürgen Schmidhuber , Proceedings of the 2016 Industrial and Systems Engineering Research Conference, ISERC 2016 ,  2018
    Multiple

    Free

  • Working hard to know your neighbor’s margins: Local descriptor learning loss

    Filip Radenovic, Jiri Matas, Anastasiya Mishchuk, Dmytro Mishkin , NeurIPS 2017 12 ,  2017
    Multiple

    Free

  • Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers

    Adam Fisch, Jiang Guo, Regina Barzilay , IJCNLP 2019 11 ,  2019
    Python

    Free

  • Words or Characters? Fine-grained Gating for Reading Comprehension

    Junjie Hu, Ye Yuan, Zhilin Yang, Bhuwan Dhingra, William W. Cohen, Ruslan Salakhutdinov , 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings ,  2016
    Python

    Free

  • Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors

    Louis-Philippe Morency, Ying Shen, Zhun Liu, Amir Zadeh, Paul Pu Liang, Yansen Wang , 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 ,  2018
    Multiple

    Free

  • Words are Malleable: Computing Semantic Shifts in Political and Media Discourse

    Hosein Azarbonyad, Kaspar Beelen, Maarten Marx, Alexandra Arkut, Jaap Kamps, Mostafa Dehghani , International Conference on Information and Knowledge Management, Proceedings ,  2017
    Jupyter Notebook

    Free

  • WordKit: a Python Package for Orthographic and Phonological Featurization

    Walter Daelemans, St{\'e}phan Tulkens, Dominiek ra, S , LREC 2018 5 ,  2018
    Python

    Free

  • word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs

    Dongwoo Kim, Kyubyong Park, Yo Joong Choe , LREC 2020 5 ,  2019
    Python

    Free

  • Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?

    Alexandre Berard, Hervé Blanchon, Laurent Besacier, Christophe Servan, Zied Elloumi , COLING 2016 12 ,  2016
    Java

    Free

  • Word2vec to behavior: morphology facilitates the grounding of language in machines

    David Matthews, Sam Kriegman, Josh Bongard, Collin Cappelle , IEEE International Conference on Intelligent Robots and Systems ,  2019
    Python

    Free

  • word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement

    Aliakbar Panahi, Seyran Saeedi, Tom Arodz , ICLR 2020 1 ,  2019
    Python

    Free

  • Word-like character n-gram embedding

    Geewook Kim, Hidetoshi Shimodaira, Kazuki Fukui , WS 2018 11 ,  2018
    C++

    Free

  • Word-level Textual Adversarial Attacking as Combinatorial Optimization

    Fanchao Qi, Maosong Sun, Zhiyuan Liu, Qun Liu, Chenghao Yang, Meng Zhang, Yuan Zang , ACL 2020 7 ,  2019
    Python

    Free

  • Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison

    Hongdong Li, Cristian Rodriguez Opazo, Xin Yu, Dongxu Li , Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 ,  2019
    Multiple

    Free

  • Word Translation Without Parallel Data

    Alexis Conneau, Marc'Aurelio Ranzato, Guillaume Lample, Hervé Jégou, Ludovic Denoyer , ICLR 2018 1 ,  2017
    Multiple

    Free

  • Word Similarity Datasets for Thai: Construction and Evaluation

    Aleksei Pulich, Ponrudee Netisopakul, Gerhard Wohlgenannt , IEEE Access ,  2019
    Unspecified

    Free

  • Word Sense Induction with Neural biLM and Symmetric Patterns

    Yoav Goldberg, Asaf Amrami , EMNLP 2018 10 ,  2018
    Jupyter Notebook

    Free

  • Word Representations via Gaussian Embedding

    Andrew McCallum, Luke Vilnis , 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings ,  2014
    Python

    Free

  • Word Ordering Without Syntax

    Allen Schmaltz, Alexander M. Rush, Stuart M. Shieber , EMNLP 2016 11 ,  2016
    HTML

    Free

  • Word Ordering as Unsupervised Learning Towards Syntactically Plausible Word Representations

    Noriki Nishida, Hideki Nakayama , IJCNLP 2017 11 ,  2017
    Python

    Free

  • Word Mover’s Embedding: From Word2Vec to Document Embedding

    Pin-Yu Chen, Avinash Balakrishnan, Kun Xu, Lingfei Wu, Ian E. H. Yen, Fangli Xu, Michael J. Witbrock, Pradeep Ravikumar , EMNLP 2018 10 ,  2018
    C

    Free

  • Word Error Rate Estimation for Speech Recognition: e-WER

    Ahmed Ali, Steve Renals , ACL 2018 7 ,  2018
    Jupyter Notebook

    Free

  • Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem

    Sven Buechel, Udo Hahn , NAACL 2018 6 ,  2018
    Python

    Free

  • Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes

    Londa Schiebinger, Dan Jurafsky, James Zou, Nikhil Garg , Proceedings of the National Academy of Sciences of the United States of America ,  2017
    Python

    Free

  • Word Embeddings for Entity-annotated Texts

    Satya Almasian, Andreas Spitz, Michael Gertz , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ,  2019
    Python

    Free

  • WONDER: Weighted one-shot distributed ridge regression in high dimensions

    Edgar Dobriban, Yue Sheng , Journal of Machine Learning Research ,  2019
    MATLAB

    Free

  • Women also Snowboard: Overcoming Bias in Captioning Models

    Trevor Darrell, Kaylee Burns, Kate Saenko, Lisa Anne Hendricks, Anna Rohrbach , ECCV 2018 9 ,  2018
    Jupyter Notebook

    Free

  • Wizard of Wikipedia: Knowledge-Powered Conversational agents

    Angela Fan, Jason Weston, Emily Dinan, Michael Auli, Kurt Shuster, Stephen Roller , ICLR 2019 5 ,  2018
    Multiple

    Free

  • With Friends Like These, Who Needs Adversaries?

    Nicholas A. Lord, Philip H. S. Torr, Saumya Jetley , NeurIPS 2018 12 ,  2018
    MATLAB

    Free

  • Wise Sliding Window Segmentation: A classification-aided approach for trajectory segmentation

    Mohammad Etemad, Stan Matwin, Zahra Etemad, Luis Torgo, Vania Bogorny, Amilcar Soares , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ,  2020
    Jupyter Notebook

    Free

  • WiRe57 : A Fine-Grained Benchmark for Open Information Extraction

    Philippe Langlais, Fabrizio Gotti, William Léchelle , WS 2019 8 ,  2018
    Python

    Free

  • WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge

    Yangqiu Song, Hongming Zhang, Xinran Zhao , ACL 2020 7 ,  2020
    Python

    Free

  • WinoGrande: An Adversarial Winograd Schema Challenge at Scale

    Yejin Choi, Ronan Le Bras, Keisuke Sakaguchi, Chandra Bhagavatula , Proceedings of the AAAI Conference on Artificial Intelligence ,  2019
    Python

    Free

  • Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks

    Muhammad Awais, Xiao-Jun Wu, Patrik Huber, Josef Kittler, Zhen-Hua Feng , CVPR 2018 6 ,  2017
    C++

    Free

  • WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation

    Nicolas Thome, Matthieu Cord, Thibaut Durand, Taylor Mordan , CVPR 2017 7 ,  2017
    Python

    Free

  • WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia

    Llion Jones, Matthew Kelcey, David Berthelot, Illia Polosukhin, Andrew Fandrianto, Alexandre Lacoste, Daniel Hewlett, Jay Han , ACL 2016 8 ,  2016
    Python

    Free

  • WikiRank: Improving Keyphrase Extraction Based on Background Knowledge

    Vincent Ng, Yang Yu , LREC 2018 - 11th International Conference on Language Resources and Evaluation ,  2018
    Python

    Free

  • WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset

    Jibril Frej, Jean-Pierre Chevallet, Didier Schwab , LREC 2020 5 ,  2019
    Python

    Free

  • WikiDragon: A Java Framework For Diachronic Content And Network Analysis Of MediaWikis

    er, Sung Y. Song, R{\"u}diger Gleim, Alex Mehler , LREC 2018 5 ,  2018
    Java

    Free

  • Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

    Zifeng Wu, Anton van den Hengel, Chunhua Shen , Pattern Recognition ,  2016
    Multiple

    Free

  • WIDER FACE: A Face Detection Benchmark

    Ping Luo, Chen Change Loy, Shuo Yang, Xiaoou Tang , CVPR 2016 6 ,  2015
    Python

    Free

  • Wide-Slice Residual Networks for Food Recognition

    Niki Martinel, Gian Luca Foresti, Christian Micheloni , Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018 ,  2016
    Multiple

    Free

  • Wide-Coverage Neural A* Parsing for Minimalist Grammars

    Shay B. Cohen, Milos Stanojevic, Mark Steedman, John Torr , ACL 2019 7 ,  2019
    Python

    Free

  • Wide-Context Semantic Image Extrapolation

    Jiaya Jia, Xiaoyong Shen, Xin Tao, Yi Wang , CVPR 2019 6 ,  2019
    Python

    Free

  • Wide Residual Networks

    Sergey Zagoruyko, Nikos Komodakis , Eurasip Journal on Wireless Communications and Networking ,  2016
    Multiple

    Free

  • Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes

    Greg Yang , NeurIPS 2019 12 ,  2019
    Jupyter Notebook

    Free

  • Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification

    Ying Tu, Jun Li, Zhi He, Haowen Luo, Shengjie Liu , IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018 7 ,  2018
    Python

    Free

  • Wide & Deep Learning for Recommender Systems

    Vihan Jain, Levent Koc, Heng-Tze Cheng, Tal Shaked, Zakaria Haque, Hrishi Aradhye, Jeremiah Harmsen, Rohan Anil, Lichan Hong, Xiaobing Liu, Glen Anderson, Wei Chai, Mustafa Ispir, Tushar Chandra, Hemal Shah, Greg Corrado , ACM International Conference Proceeding Series ,  2016
    Multiple

    Free

  • Why We Need New Evaluation Metrics for NLG

    Amanda Cercas Curry, Verena Rieser, Ondřej Dušek, Jekaterina Novikova , EMNLP 2017 9 ,  2017
    R

    Free

  • Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem

    Matthias Hein, Julian Bitterwolf, Maksym Andriushchenko , CVPR 2019 6 ,  2018
    Python

    Free

  • Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks

    Eunho Yang, Joonyoung Yi, Juhyuk Lee, Kwang Joon Kim, Sung Ju Hwang , ICLR 2020 1 ,  2019
    Unspecified

    Free

  • Why Having 10,000 Parameters in Your Camera Model Is Better Than Twelve

    Marc Pollefeys, Viktor Larsson, Thomas Schops, Torsten Sattler , CVPR 2020 6 ,  2020
    C++

    Free

  • Why gradient clipping accelerates training: A theoretical justification for adaptivity

    Tianxing He, Ali Jadbabaie, Jingzhao Zhang, Suvrit Sra , ICLR 2020 1 ,  2019
    Python

    Free

  • Why does deep and cheap learning work so well?

    David Rolnick, Henry W. Lin, Max Tegmark , Journal of Statistical Physics ,  2016
    Unspecified

    Free

  • Why do deep convolutional networks generalize so poorly to small image transformations?

    Aharon Azulay, Yair Weiss , ICLR 2019 5 ,  2018
    Multiple

    Free

  • Why Can’t I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition

    Jia-Bin Huang, Joseph C. E. Messou, Chen Gao, Jinwoo Choi , NeurIPS 2019 12 ,  2019
    Python

    Free

  • Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER

    Peng-Hsuan Li, Wei-Yun Ma, Tsu-Jui Fu , Proceedings of the AAAI Conference on Artificial Intelligence ,  2019
    Multiple

    Free

  • Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps

    Beomsu Kim, Junghoon Seo, Taegyun Jeon, Jeongyeol Choe, Jamyoung Koo, SeungHyun Jeon , Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019 ,  2019
    Jupyter Notebook

    Free

  • Whole-Body Human Pose Estimation in the Wild

    Sheng Jin, Jin Xu, Chen Qian, Lumin Xu, Ping Luo, Wanli Ouyang, Can Wang, Wentao Liu , ECCV 2020 8 ,  2020
    Python

    Free

  • Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks

    Nicholas Hawkins, Jitendra Jonnagaddala, Junzhou Huang, Jiawen Yao, Xinliang Zhu , Medical Image Analysis ,  2020
    Multiple

    Free

  • Whole MILC: generalizing learned dynamics across tasks, datasets, and populations

    Sergey M. Plis, Zening Fu, Md Mahfuzur Rahman, Usman Mahmood, Alex Fedorov, Noah Lewis, Vince D. Calhoun , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ,  2020
    Jupyter Notebook

    Free

  • Who's Afraid of Adversarial Queries? The Impact of Image Modifications on Content-based Image Retrieval

    Zhuoran Liu, Zhengyu Zhao, Martha Larson , ICMR 2019 - Proceedings of the 2019 ACM International Conference on Multimedia Retrieval ,  2019
    Python

    Free

  • Who Let The Dogs Out? Modeling Dog Behavior From Visual Data

    Hessam Bagherinezhad, Joseph Redmon, Kiana Ehsani, Roozbeh Mottaghi, Ali Farhadi , CVPR 2018 6 ,  2018
    Python

    Free

  • Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models

    Stefano Sarao Mannelli, Giulio Biroli, Florent Krzakala, Lenka Zdeborová, Chiara Cammarota , NeurIPS 2019 12 ,  2019
    C++

    Free

  • Who Blames Whom in a Crisis? Detecting Blame Ties from News Articles Using Neural Networks

    Shuailong Liang, Yue Zhang, Olivia Nicol , 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 ,  2019
    Python

    Free

  • Whitening-Free Least-Squares Non-Gaussian Component Analysis

    Hiroaki Shiino, Hiroaki Sasaki, Gang Niu, Masashi Sugiyama , Journal of Machine Learning Research ,  2016
    Matlab

    Free

  • Whitening and Coloring batch transform for GANs

    Nicu Sebe, Enver Sangineto, Aliaksandr Siarohin , ICLR 2019 5 ,  2018
    Python

    Free

  • White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks

    Yoav Chai, Yotam Gil, Or Gorodissky, Jonathan Berant , NAACL 2019 6 ,  2019
    Python

    Free

  • Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial Networks

    Jose A. Gonzalez, Santiago Pascual, Antonio Bonafonte, Joan Serrà , IberSPEECH 2018 ,  2018
    Multiple

    Free

  • Which Training Methods for GANs do actually Converge?

    Lars Mescheder, Andreas Geiger, Sebastian Nowozin , ICML 2018 7 ,  2018
    Multiple

    Free

  • Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?

    Shizhu He, Jun Zhao, Xiangrong Zeng, Kang Liu, Shangmin Guo , EACL 2017 4 ,  2017
    Java

    Free

  • Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?

    Weisi Lin, Ming Jiang, Tingting Jiang, Dingquan Li , IEEE Transactions on Multimedia 2018 10 ,  2018
    MATLAB

    Free

  • Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model

    Zachary Nado, Guodong Zhang, Lala Li, George E. Dahl, Roger Grosse, Christopher J. Shallue, James Martens, Sushant Sachdeva , NeurIPS 2019 12 ,  2019
    Jupyter Notebook

    Free

  • Where to put the Image in an Image Caption Generator

    Albert Gatt, Kenneth P. Camilleri, Marc Tanti , Natural Language Engineering ,  2017
    Multiple

    Free

  • Where to Explore Next? ExHistCNN for History-aware Autonomous 3D Exploration

    Alessio Del Bue, Yiming Wang , ECCV 2020 8 ,  2020
    Python

    Free

  • Where is Your Evidence: Improving Fact-checking by Justification Modeling

    Smar Muresan, a, Savvas Petridis, Tariq Alhindi , WS 2018 11 ,  2018
    Multiple

    Free

  • Where is my URI?

    Andre Valdestilhas, Markus Nentwig, Tommaso Soru, Edgard Marx, Axel-Cyrille Ngonga Ngomo, Muhammad Saleem , European Semantic Web Conference 2018 6 ,  2018
    Java

    Free

  • Where Is My Mirror?

    Ke Xu, Haiyang Mei, Xin Yang, Rynson W. H. Lau, Baocai Yin, Xiaopeng Wei , ICCV 2019 10 ,  2019
    Python

    Free

  • Where is Misty? Interpreting Spatial Descriptors by Modeling Regions in Space

    Dan Klein, Nikita Kitaev , EMNLP 2017 9 ,  2017
    JavaScript

    Free

  • Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences

    Lianli Gao, Zhou Zhao, Yang Zhao, Zhu Zhang, Qi Wang, Huasheng Liu , CVPR 2020 6 ,  2020
    Unspecified

    Free

  • Where are we now? A large benchmark study of recent symbolic regression methods

    Patryk Orzechowski, Jason H. Moore, William La Cava , GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference ,  2018
    Jupyter Notebook

    Free

  • Where are the Masks: Instance Segmentation with Image-level Supervision

    Mark Schmidt, Issam H. Laradji, David Vazquez , 30th British Machine Vision Conference 2019, BMVC 2019 ,  2019
    Python

    Free

  • Where are the Blobs: Counting by Localization with Point Supervision

    Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, Mark W. Schmidt , European Conference on Computer Vision ,  2018
    PyTorch

    Free

  • Where are the Blobs: Counting by Localization with Point Supervision

    Pedro O. Pinheiro, David Vazquez, Issam H. Laradji, Mark Schmidt, Negar Rostamzadeh , ECCV 2018 9 ,  2018
    Multiple

    Free

  • When2com: Multi-Agent Perception via Communication Graph Grouping

    Junjiao Tian, Nathaniel Glaser, Zsolt Kira, Yen-Cheng Liu , CVPR 2020 6 ,  2020
    Python

    Free

  • When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos

    David Crandall, Yu Yao, Xizi Wang, Zelin Pu, Mingze Xu, Ella Atkins , arXiv preprint ,  2020
    Multiple

    Free

  • When Unsupervised Domain Adaptation Meets Tensor Representations

    Zhiguo Cao, Anton van den Hengel, Hao Lu, Wei Wei, Lei Zhang, Ke Xian, Chunhua Shen , ICCV 2017 10 ,  2017
    Matlab

    Free

  • When to Trust Your Model: Model-Based Policy Optimization

    Marvin Zhang, Sergey Levine, Justin Fu, Michael Janner , NeurIPS 2019 12 ,  2019
    Python

    Free

  • When to reply? Context Sensitive Models to Predict Instructor Interventions in MOOC Forums

    Min-Yen Kan, Muthu Kumar Chandrasekaran , arXiv preprint ,  2019
    Python

    Free

  • When Relation Networks meet GANs: Relation GANs with Triplet Loss

    Yizhou Yu, Yue Wang, Lijun Wang, Runmin Wu, Pingping Zhang, Huchuan Lu, Kunyao Zhang , arXiv preprint ,  2020
    Python

    Free

  • When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks

    Ziwei Liu, Rui Xu, Dahua Lin, Yuzhe Yang, Minghao Guo , CVPR 2020 6 ,  2019
    Python

    Free

  • When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach

    Xianming Liu, Ding Liu, Bihan Wen, Thomas S. Huang, Zhangyang Wang , IJCAI International Joint Conference on Artificial Intelligence ,  2017
    Multiple

    Free

  • When Explanations Lie: Why Many Modified BP Attributions Fail

    Tim Landgraf, Maximilian Granz, Leon Sixt , ICML 2020 1 ,  2019
    Jupyter Notebook

    Free

  • When Does Self-supervision Improve Few-shot Learning?

    Subhransu Maji, Bharath Hariharan, Jong-Chyi Su , ECCV 2020 8 ,  2019
    Python

    Free

  • When Does Self-Supervision Help Graph Convolutional Networks?

    Tianlong Chen, Yuning You, Yang Shen, Zhangyang Wang , ICML 2020 1 ,  2020
    Python

    Free

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