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Representation learning promises to unlock deep learning for the long tail of vision tasks without expensive labelled datasets.
Scaling and benchmarking self-supervised visual representation learning
Goyal, P., Mahajan, D., Gupta, A., and Misra, I · 1905
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The treatment of ties in ranking problems
Kendall, M. G · 1945
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Rank aggregation methods for the web
Dwork, C., Kumar, R., Naor, M., and Sivakumar, D · 2001
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Learning methods for generic object recognition with invariance to pose and lighting
LeCun, Y., Huang, F. J., and Bottou, L · 2004
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One-shot learning of object categories
Fei-Fei, L., Fergus, R., and Perona, P · 2006
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One-shot learning of object categories
Li, F.-F., Fergus, R., and Perona, P · 2006
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Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A · 2008
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Semi-supervised learning
Chapelle, O., Scholkopf, B., and Zien, A · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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A survey on transfer learning
Pan, S. J. and Yang, Q · 2009
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Sun database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., and Torralba, A · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Cats and dogs
Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C. V · 2012
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Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
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Vision meets robotics: The kitti dataset
Geiger, A., Lenz, P., Stiller, C., and Urtasun, R · 2013
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
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Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., , and Vedaldi, A · 2014
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Discriminative unsupervised feature learning with convolutional neural networks
Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., and Brox, T · 2014
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Semi-supervised learning with deep generative models
Kingma, D. P., Mohamed, S., Rezende, D. J., and Welling, M · 2014
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Cnn features off-the-shelf: An astounding baseline for recognition
Sharif Razavian, A., Azizpour, H., Sullivan, J., and Carlsson, S · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
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Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., and Efros, A. A · 2015
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Kaggle diabetic retinopathy detection, July 2015
Kaggle and EyePacs · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 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
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Beattie, C., Leibo, J. Z., Teplyashin, D., Ward, T., Wainwright, M., Küttler, H., Lefrancq, A., Green, S., Valdés, V., Sadik, A., et al · 2016
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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What makes imagenet good for transfer learning?
Huh, M., Agrawal, P., and Efros, A. A · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Electoral systems used around the world
Shahandashti, S. F · 2016
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Colorful image colorization
Zhang, R., Isola, P., and Efros, A. A · 2016
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Neural optimizer search with reinforcement learning
Bello, I., Zoph, B., Vasudevan, V., and Le, Q. V · 2017
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Assessing generative models via precision and recall
Sajjadi, M. S., Bachem, O., Lucic, M., Bousquet, O., and Gelly, S · 2018
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Wasserstein auto-encoders
Tolstikhin, I., Bousquet, O., Gelly, S., and Schoelkopf, B · 2018
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Recent advances in autoencoder-based representation learning
Tschannen, M., Bachem, O., and Lucic, M · 2018
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Rotation equivariant cnns for digital pathology
Veeling, B. S., Linmans, J., Winkens, J., Cohen, T., and Welling, M · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2018
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Deep visual domain adaptation: A survey
Wang, M. and Deng, W · 2018
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Remote sensing image scene classification: Benchmark and state of the art
Cheng, G., Han, J., and Lu, X · 2017
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Multi-task self-supervised visual learning
Doersch, C. and Zisserman, A · 2017
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Adversarial feature learning
Donahue, J., Krähenbühl, P., and Darrell, T · 2017
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Adversarially learned inference
Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., and Courville, A · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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GANs trained by a two time-scale update rule converge to a Nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Klambauer, G., and Hochreiter, S · 2017
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
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Self-supervised gans via auxiliary rotation loss
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Autoaugment: Learning augmentation policies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2019
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Large scale adversarial representation learning
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
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Data-efficient image recognition with contrastive predictive coding
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Revisiting self-supervised visual representation learning
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Do better imagenet models transfer better?
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Challenging common assumptions in the unsupervised learning of disentangled representations
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Decoupled weight decay regularization
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High-fidelity image generation with fewer labels
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In-domain representation learning for remote sensing
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Transfusion: Understanding transfer learning with applications to medical imaging
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Classification accuracy score for conditional generative models
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Do ImageNet classifiers generalize to ImageNet?
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Practical and consistent estimation of f-divergences
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Efficientnet: Rethinking model scaling for convolutional neural networks
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Metric learning for patch classification in digital pathology
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Meta-dataset: A dataset of datasets for learning to learn from few examples
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On mutual information maximization for representation learning
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S 4 L: Self-Supervised Semi-Supervised Learning
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