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Self-Supervised Learning (SSL) has become a very active area of Deep Learning research where it is heavily used as a pre-training method for classification and other tasks.
The application of Bayesian methods for seeking the extremum
Mockus, J., Tiesis, V., and Zilinskas, A · 1978
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Smart(sampling)augment: Optimal and efficient data augmentation for semantic segmentation
Negassi, M., Wagner, D., and Reiterer, A · 1999
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Improved baselines with momentum contrastive learning
Chen, X., Fan, H., Girshick, R., and He, K · 2003
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Histograms of oriented gradients for human detection
Dalal, N. and Triggs, B · 2005
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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An efficient approach for assessing hyperparameter importance
Hutter, F., Hoos, H., and Leyton-Brown, K · 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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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Context encoders: Feature learning by inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., and Efros, A. A · 2016
Cited alongside, same era.
Taking the human out of the loop: A review of Bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R., and de Freitas, N · 2016
Cited alongside, same era.
Zhang, R., Isola, P., and Efros, A. A · 2016
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
Giradis, S., Singh, P., and Komodakis, N · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
van den Oord, A., Li, Y., and Vinyals, O · 2018
Cited alongside, same era.
Exploring simple siamese representation learning
Chen, X. and He, K · 2021
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π \pi bo: Augmenting acquisition functions with user beliefs for bayesian optimization
Hvarfner, C., Stoll, D., Souza, A., Lindauer, M., Hutter, F., and Nardi, L · 2021
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Trivialaugment: Tuning-free yet state-of-the-art data augmentation
Müller, S. G. and Hutter, F · 2021
Later among the works it cites.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Later among the works it cites.
Meta-learning to improve pre-training
Raghu, A., Lorraine, J., Kornblith, S., McDermott, M., and Duvenaud, D. K · 2021
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Selfaugment: Automatic augmentation policies for self-supervised learning
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Cubuk, E., Zoph, B., Mane, D., Vasudevan, V., and Le, Q · 2019
Cited alongside, same era.
Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E., Zoph, B., Shlens, J., and Le, Q · 2020
Cited alongside, same era.
Bootstrap your own latent-a new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G
Cited in the paper.
Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis
Yang, J., Shi, R., and Ni, B
Cited in the paper.
Medmnist v2: A large-scale lightweight benchmark for 2d and 3d biomedical image classification
Yang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., Pfister, H., and Ni, B
Cited in the paper.
Reed, C. J., Metzger, S., Srinivas, A., Darrell, T., and Keutzer, K · 2021
Later among the works it cites.
Neural Pipeline Search (NEPS), May 2022
Stoll, D., Schrodi, S., Janowski, M., Mallik, N., Théophane, V., and Hutter, F · 2022
Closest in time.
Albumentations: Fast and flexible image augmentations
Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A. A · 2078
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