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This paper introduces a structured, adaptive-length deep representation called Neural Eigenmap.
Signature verification using a” siamese” time delay neural network
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah · 1993
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Multidimensional scaling
J Douglas Carroll and Phipps Arabie · 1998
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Nonlinear dimensionality reduction by locally linear embedding
Sam T Roweis and Lawrence K Saul · 2000
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Normalized cuts and image segmentation
Jianbo Shi and Jitendra Malik · 2000
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Laplacian eigenmaps for dimensionality reduction and data representation
Mikhail Belkin and Partha Niyogi · 2003
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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The mir flickr retrieval evaluation
Mark J Huiskes and Michael S Lew · 2008
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Nus-wide: a real-world web image database from national university of singapore
Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Dimension reduction: A guided tour
Christopher JC Burges et al · 2010
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
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Eigenvalues of the laplacian and their relationship to the connectedness of a graph
Anne Marsden · 2013
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Unsupervised learning of visual representations using videos
Xiaolong Wang and Abhinav Gupta · 2015
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Learning features by watching objects move
Deepak Pathak, Ross Girshick, Piotr Dollár, Trevor Darrell, and Bharath Hariharan · 2017
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Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Automatic differentiation in machine learning: a survey
Atilim Gunes Baydin, Barak A Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2018
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Unsupervised representation learning by predicting image rotations
Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, Jean Ponce, and Yann LeCun · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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Provable guarantees for self-supervised deep learning with spectral contrastive loss
Jeff Z HaoChen, Colin Wei, Adrien Gaidon, and Tengyu Ma · 2021
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Predicting what you already know helps: Provable self-supervised learning
Jason D Lee, Qi Lei, Nikunj Saunshi, and Jiacheng Zhuo · 2021
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Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Detectron
Ross Girshick, Ilija Radosavovic, Georgia Gkioxari, Piotr Dollár, and Kaiming He · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
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A theoretical analysis of contrastive unsupervised representation learning
Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, and Nikunj Saunshi · 2019
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For self-supervised learning, rationality implies generalization, provably
Yamini Bansal, Gal Kaplun, and Boaz Barak · 2020
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Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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How much can clip benefit vision-and-language tasks?
Sheng Shen, Liunian Harold Li, Hao Tan, Mohit Bansal, Anna Rohrbach, Kai-Wei Chang, Zhewei Yao, and Kurt Keutzer · 2021
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Contrastive learning, multi-view redundancy, and linear models
Christopher Tosh, Akshay Krishnamurthy, and Daniel Hsu · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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Masked siamese networks for label-efficient learning
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Mike Rabbat, and Nicolas Ballas · 2022
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Randall Balestriero and Yann LeCun · 2022
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Vicregl: Self-supervised learning of local visual features
Adrien Bardes, Jean Ponce, and Yann LeCun · 2022
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Neuralef: Deconstructing kernels by deep neural networks
Zhijie Deng, Jiaxin Shi, and Jun Zhu · 2022
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On the duality between contrastive and non-contrastive self-supervised learning
Quentin Garrido, Yubei Chen, Adrien Bardes, Laurent Najman, and Yann Lecun · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Contrastive learning can find an optimal basis for approximately invariant functions
Daniel D. Johnson, Ayoub El Hanchi, and Chris J. Maddison · 2022
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Deep contrastive learning is provably (almost) principal component analysis
Yuandong Tian · 2022
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Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Buesing, Razvan Pascanu, Charles Blundell, and Jovana Mitrovic · 2022
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Mugs: A multi-granular self-supervised learning framework
Pan Zhou, Yichen Zhou, Chenyang Si, Weihao Yu, Teck Khim Ng, and Shuicheng Yan · 2022
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Eva: Exploring the limits of masked visual representation learning at scale
Yuxin Fang, Wen Wang, Binhui Xie, Quan Sun, Ledell Wu, Xinggang Wang, Tiejun Huang, Xinlong Wang, and Yue Cao · 2023
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Contrastive learning can find an optimal basis for approximately view-invariant functions
Daniel D. Johnson, Ayoub El Hanchi, and Chris J. Maddison · 2023
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