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Contrastive loss has significantly improved performance in supervised classification tasks by using a multi-viewed framework that leverages augmentation and label information.
Benchmarking neural network robustness to common corruptions and perturbations
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SCAN: A scalable neural networks framework towards compact and efficient models
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What makes for good views for contrastive learning
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Unsupervised learning of visual features by contrasting cluster assignments
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Bootstrap your own latent: A new approach to self-supervised learning
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Automated flower classification over a large number of classes
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Learning multiple layers of features from tiny images
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Recognizing indoor scenes
Quattoni, A.; and Torralba, A. 2009 · 2009
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The pascal visual object classes (voc) challenge
Everingham, M.; Van Gool, L.; Williams, C. K.; Winn, J.; and Zisserman, A. 2010 · 2010
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MixCo: Mix-up Contrastive Learning for Visual Representation
Kim, S.; Lee, G.; Bae, S.; and Yun, S.-Y. 2020 · 2010
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I-mix: A domain-agnostic strategy for contrastive representation learning
Lee, K.; Zhu, Y.; Sohn, K.; Li, C.-L.; Shin, J.; and Lee, H. 2020 · 2010
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Estimating divergence functionals and the likelihood ratio by convex risk minimization
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Exploring Simple Siamese Representation Learning
Chen, X.; and He, K. 2020 · 2011
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Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning
Gunel, B.; Du, J.; Conneau, A.; and Stoyanov, V. 2020 · 2011
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Novel dataset for fine-grained image categorization: Stanford dogs
Khosla, A.; Jayadevaprakash, N.; Yao, B.; and Li, F.-F. 2011 · 2011
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The caltech-ucsd birds-200-2011 dataset
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Human action recognition by learning bases of action attributes and parts
Yao, B.; Jiang, X.; Khosla, A.; Lin, A. L.; Guibas, L.; and Fei-Fei, L. 2011 · 2011
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Cats and dogs
Parkhi, O. M.; Vedaldi, A.; Zisserman, A.; and Jawahar, C. 2012 · 2012
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Collecting a large-scale dataset of fine-grained cars
Krause, J.; Deng, J.; Stark, M.; and Fei-Fei, L. 2013 · 2013
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Fine-grained visual classification of aircraft
Maji, S.; Rahtu, E.; Kannala, J.; Blaschko, M.; and Vedaldi, A. 2013 · 2013
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Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D.; and Fergus, R. 2014 · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
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Representation learning with contrastive predictive coding
Oord, A. v. d.; Li, Y.; and Vinyals, O. 2018 · 2018
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Variational information distillation for knowledge transfer
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Autoaugment: Learning augmentation strategies from data
Cubuk, E. D.; Zoph, B.; Mane, D.; Vasudevan, V.; and Le, Q. V. 2019 · 2019
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Estimating Information Flow in Deep Neural Networks
Goldfeld, Z.; van den Berg, E.; Greenewald, K. H.; Melnyk, I.; Nguyen, N.; Kingsbury, B.; and Polyanskiy, Y. 2019 · 2019
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Distillation-based training for multi-exit architectures
Phuong, M.; and Lampert, C. H. 2019 · 2019
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On variational bounds of mutual information
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Tiny imagenet visual recognition challenge
Le, Y.; and Yang, X. 2015 · 2015
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Deeply-supervised nets
Lee, C.-Y.; Xie, S.; Gallagher, P.; Zhang, Z.; and Tu, Z. 2015 · 2015
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Semi-supervised learning with ladder networks
Rasmus, A.; Valpola, H.; Honkala, M.; Berglund, M.; and Raiko, T. 2015 · 2015
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Going deeper with convolutions
Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; and Rabinovich, A. 2015 · 2015
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Deep learning and the information bottleneck principle
Tishby, N.; and Zaslavsky, N. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Large-margin softmax loss for convolutional neural networks
Liu, W.; Wen, Y.; Yu, Z.; and Yang, M. 2016 · 2016
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Poole, B.; Ozair, S.; Van Den Oord, A.; Alemi, A.; and Tucker, G. 2019 · 2019
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On the information bottleneck theory of deep learning
Saxe, A. M.; Bansal, Y.; Dapello, J.; Advani, M.; Kolchinsky, A.; Tracey, B. D.; and Cox, D. D. 2019 · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M.; and Le, Q. 2019 · 2019
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A simple framework for contrastive learning of visual representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020 · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E. D.; Zoph, B.; Shlens, J.; and Le, Q. V. 2020 · 2020
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Momentum contrast for unsupervised visual representation learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. 2020 · 2020
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On the noisy gradient descent that generalizes as sgd
Wu, J.; Hu, W.; Xiong, H.; Huan, J.; Braverman, V.; and Zhu, Z. 2020 · 2020
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Loco: Local contrastive representation learning
Xiong, Y.; Ren, M.; and Urtasun, R. 2020 · 2020
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Resolution adaptive networks for efficient inference
Yang, L.; Han, Y.; Chen, X.; Song, S.; Dai, J.; and Huang, G. 2020 · 2020
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Knowledge transfer via dense cross-layer mutual-distillation
Yao, A.; and Sun, D. 2020 · 2020
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Seed: Self-supervised distillation for visual representation
Fang, Z.; Wang, J.; Wang, L.; Zhang, L.; Yang, Y.; and Liu, Z. 2021 · 2021
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Disco: Remedy self-supervised learning on lightweight models with distilled contrastive learning
Gao, Y.; Zhuang, J.-X.; Li, K.; Cheng, H.; Guo, X.; Huang, F.; Ji, R.; and Sun, X. 2021 · 2021
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Dissecting supervised constrastive learning
Graf, F.; Hofer, C.; Niethammer, M.; and Kwitt, R. 2021 · 2021
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Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning
Kaku, A.; Upadhya, S.; and Razavian, N. 2021 · 2021
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Anytime inference with distilled hierarchical neural ensembles
Ruiz, A.; and Verbeek, J. 2021 · 2021
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Decomposed Mutual Information Estimation for Contrastive Representation Learning
Sordoni, A.; Dziri, N.; Schulz, H.; Gordon, G.; Bachman, P.; and Des Combes, R. T. 2021 · 2021
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Towards domain-agnostic contrastive learning
Verma, V.; Luong, T.; Kawaguchi, K.; Pham, H.; and Le, Q. 2021 · 2021
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Exploring cross-image pixel contrast for semantic segmentation
Wang, W.; Zhou, T.; Yu, F.; Dai, J.; Konukoglu, E.; and Van Gool, L. 2021 · 2021
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Self-regulation for semantic segmentation
Zhang, D.; Zhang, H.; Tang, J.; Hua, X.-S.; and Sun, Q. 2021 · 2021
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Weakly supervised contrastive learning
Zheng, M.; Wang, F.; You, S.; Qian, C.; Zhang, C.; Wang, X.; and Xu, C. 2021 · 2021
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Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning
Chen, M.; Fu, D. Y.; Narayan, A.; Zhang, M.; Song, Z.; Fatahalian, K.; and Ré, C. 2022 · 2022
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Targeted supervised contrastive learning for long-tailed recognition
Li, T.; Cao, P.; Yuan, Y.; Fan, L.; Yang, Y.; Feris, R. S.; Indyk, P.; and Katabi, D. 2022 · 2022
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