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Masked autoencoders have become popular training paradigms for self-supervised visual representation learning.
Singular value decomposition and principal component analysis
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Histograms of oriented gradients for human detection
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Automated flower classification over a large number of classes
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ImageNet: A large-scale hierarchical image database
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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3d object representations for fine-grained categorization
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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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Fast R-CNN
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Distilling the knowledge in a neural network
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Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger · 2016
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SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
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The inaturalist species classification and detection dataset
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Unified perceptual parsing for scene understanding
Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian Sun · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
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Cascade R-CNN: high quality object detection and instance segmentation
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
An empirical study of training self-supervised vision transformers
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Learning transferable visual models from natural language supervision
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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Zero-shot text-to-image generation
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Training data-efficient image transformers & distillation through attention
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Relational knowledge distillation
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Contrastive representation distillation
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Practical automated data augmentation with a reduced search space
ED Cubuk, B Zoph, J Shlens, and Q Le Randaugment · 2020
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Data2vec: A general framework for self-supervised learning in speech, vision and language
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Masked autoencoders are scalable vision learners
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Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and localization
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Masked feature prediction for self-supervised visual pre-training
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Contrastive learning rivals masked image modeling in fine-tuning via feature distillation
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SimMIM: A simple framework for masked image modeling
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