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Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks.
D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song · 1907
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Self-organizing neural network that discovers surfaces in random-dot stereograms
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Signature verification using a “siamese” time delay neural network
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Improved baselines with momentum contrastive learning
X. Chen, H. Fan, R. Girshick, and K. He · 2003
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Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
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Extracting and composing robust features with denoising autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol · 2008
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Frequency-tuned salient region detection
R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk · 2009
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Learning to Detect a Salient Object
T. Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, and H.-Y. Shum · 2010
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Torchvision the machine-vision package of torch
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ImageNet Classification with Deep Convolutional Neural Networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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Hierarchical Saliency Detection
Q. Yan, L. Xu, J. Shi, and J. Jia · 2013
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Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox · 2014
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
D. L. K. Yamins, H. Hong, C. F. Cadieu, E. A. Solomon, D. Seibert, and J. J. DiCarlo · 2014
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FlowNet: Learning Optical Flow With Convolutional Networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox · 2015
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Explaining and harnessing adversarial examples
I. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
S. Ioffe and C. Szegedy · 2015
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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The Cityscapes Dataset for Semantic Urban Scene Understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Synthetic data for text localisation in natural images
A. Gupta, A. Vedaldi, and A. Zisserman · 2016
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Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Adversarial examples in the physical world
A. Kurakin, I. J. Goodfellow, and S. Bengio · 2016
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Deepsaliency: Multi-task deep neural network model for salient object detection
X. Li, L. Zhao, L. Wei, M.-H. Yang, F. Wu, Y. Zhuang, H. Ling, and J. Wang · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
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Context Encoders: Feature Learning by Inpainting
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros · 2016
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Saliency detection with recurrent fully convolutional networks
L. Wang, L. Wang, H. Lu, P. Zhang, and X. Ruan · 2016
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Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
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Multi-task Self-Supervised Visual Learning
C. Doersch and A. Zisserman · 2017
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Cut, paste and learn: Surprisingly easy synthesis for instance detection
D. Dwibedi, I. Misra, and M. Hebert · 2017
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Synthesizing training data for object detection in indoor scenes
G. Georgakis, A. Mousavian, A. C. Berg, and J. Kosecka · 2017
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Mask R-CNN
K. He, G. Gkioxari, P. Dollar, and R. Girshick · 2017
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Deeply supervised salient object detection with short connections
Q. Hou, M.-M. Cheng, X. Hu, A. Borji, Z. Tu, and P. H. Torr · 2017
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Measuring the tendency of cnns to learn surface statistical regularities
J. Jo and Y. Bengio · 2017
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Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
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Non-local deep features for salient object detection
Z. Luo, A. Mishra, A. Achkar, J. Eichel, S. Li, and P.-M. Jodoin · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
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Large batch training of convolutional networks
Y. You, I. Gitman, and B. Ginsburg · 2017
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Dilated residual networks
F. Yu, V. Koltun, and T. Funkhouser · 2017
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Recognition in terra incognita
S. Beery, G. Van Horn, and P. Perona · 2018
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Deep clustering for unsupervised learning of visual features
M. Caron, P. Bojanowski, A. Joulin, and M. Douze · 2018
Self-labelling via simultaneous clustering and representation learning
Y. M. Asano, C. Rupprecht, and A. Vedaldi · 2020
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L. Beyer, O. J. Hénaff, A. Kolesnikov, X. Zhai, and A. v. d. Oord · 2020
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Are all negatives created equal in contrastive instance discrimination?
T. T. Cai, J. Frankle, D. J. Schwab, A. S. Morcos, et al · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin · 2020
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Exploring simple siamese representation learning
X. Chen and K. He · 2020
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Modeling visual context is key to augmenting object detection datasets
N. Dvornik, J. Mairal, and C. Schmid · 2018
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Unsupervised representation learning by predicting image rotations
S. Gidaris, P. Singh, and N. Komodakis · 2018
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Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2018
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
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Representation Learning with Contrastive Predictive Coding
A. v. d. Oord, Y. Li, and O. Vinyals · 2018
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Learning to segment via cut-and-paste
T. Remez, J. Huang, and M. Brown · 2018
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On the surprising similarities between supervised and self-supervised models
R. Geirhos, K. Narayanappa, B. Mitzkus, M. Bethge, F. A. Wichmann, and W. Brendel · 2020
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Simple copy-paste is a strong data augmentation method for instance segmentation
G. Ghiasi, Y. Cui, A. Srinivas, R. Qian, T.-Y. Lin, E. D. Cubuk, Q. V. Le, and B. Zoph · 2020
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Watching the world go by: Representation learning from unlabeled videos
D. Gordon, K. Ehsani, D. Fox, and A. Farhadi · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. D. Guo, M. G. Azar, et al · 2020
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Array programming with NumPy
C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant · 2020
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Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
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Data-efficient image recognition with contrastive predictive coding
O. Hénaff, A. Srinivas, J. De Fauw, A. Razavi, C. Doersch, S. M. A. Eslami, and A. v. d. Oord · 2020
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The origins and prevalence of texture bias in convolutional neural networks
K. L. Hermann, T. Chen, and S. Kornblith · 2020
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Boosting contrastive self-supervised learning with false negative cancellation
T. Huynh, S. Kornblith, M. R. Walter, M. Maire, and M. Khademi · 2020
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Self-supervised visual feature learning with deep neural networks: A survey
L. Jing and Y. Tian · 2020
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Hard negative mixing for contrastive learning
Y. Kalantidis, M. B. Sariyildiz, N. Pion, P. Weinzaepfel, and D. Larlus · 2020
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Self-supervised learning of pretext-invariant representations
I. Misra and L. van der Maaten · 2020
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Evaluating prediction-time batch normalization for robustness under covariate shift
Z. Nado, S. Padhy, D. Sculley, A. D’Amour, B. Lakshminarayanan, and J. Snoek · 2020
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Demystifying contrastive self-supervised learning: Invariances, augmentations and dataset biases
S. Purushwalkam and A. Gupta · 2020
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U2-net: Going deeper with nested u-structure for salient object detection
X. Qin, Z. Zhang, C. Huang, M. Dehghan, O. Zaiane, and M. Jagersand · 2020
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Foolbox native: Fast adversarial attacks to benchmark the robustness of machine learning models in pytorch, tensorflow, and jax
J. Rauber, R. Zimmermann, M. Bethge, and W. Brendel · 2020
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Improving robustness against common corruptions by covariate shift adaptation
S. Schneider, E. Rusak, L. Eck, O. Bringmann, W. Brendel, and M. Bethge · 2020
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Time for a background check! uncovering the impact of background features on deep neural networks
V. Sehwag, R. Oak, M. Chiang, and P. Mittal · 2020
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Casting your model: Learning to localize improves self-supervised representations
R. R. Selvaraju, K. Desai, J. Johnson, and N. Naik · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
K. Sohn, D. Berthelot, C.-L. Li, Z. Zhang, N. Carlini, E. D. Cubuk, A. Kurakin, H. Zhang, and C. Raffel · 2020
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Unsupervised learning from video with deep neural embeddings
C. Zhuang, T. She, A. Andonian, M. S. Mark, and D. Yamins · 2020
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Emerging properties in self-supervised vision transformers
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin · 2021
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An empirical study of training self-supervised vision transformers
X. Chen, S. Xie, and K. He · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, and S. Gelly · 2021
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The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
D. Hendrycks, S. Basart, N. Mu, S. Kadavath, F. Wang, E. Dorundo, R. Desai, T. Zhu, S. Parajuli, M. Guo, D. Song, J. Steinhardt, and J. Gilmer · 2021
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Contrastive learning with hard negative samples
J. D. Robinson, C.-Y. Chuang, S. Sra, and S. Jegelka · 2021
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Viewmaker networks: Learning views for unsupervised representation learning
A. Tamkin, M. Wu, and N. Goodman · 2021
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Conditional negative sampling for contrastive learning of visual representations
M. Wu, M. Mosse, C. Zhuang, D. Yamins, and N. Goodman · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
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Distilling localization for self-supervised representation learning
N. Zhao, Z. Wu, R. W. Lau, and S. Lin · 2021
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