Fetching the paper…
Reading the bibliography…
In this paper, we focus on the out-of-distribution (OOD) generalization of self-supervised learning (SSL).
The central role of the propensity score in observational studies for causal effects
Rosenbaum, P. R. and Rubin, D. B · 1981
Earlier work this paper cites.
A new metric for probability distributions
Endres, D. M. and Schindelin, J. E · 2003
Earlier work this paper cites.
Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A · 2006
Earlier work this paper cites.
Bootstrap your own latent: A new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al · 2006
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Causality
Pearl, J · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A · 2010
Earlier work this paper cites.
An introduction to propensity score methods for reducing the effects of confounding in observational studies
Austin, P. C · 2011
Earlier work this paper cites.
Ava: A large-scale database for aesthetic visual analysis
Murray, N., Marchesotti, L., and Perronnin, F · 2012
Earlier work this paper cites.
Density estimation in infinite dimensional exponential families
Sriperumbudur, B., Fukumizu, K., Gretton, A., Hyvärinen, A., and Kumar, R · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al · 2016
Earlier work this paper cites.
Variational inference: A review for statisticians
Blei, D. M., Kucukelbir, A., and McAuliffe, J. D · 2017
Earlier work this paper cites.
Mask r-cnn
He, K., Gkioxari, G., Dollár, P., and Girshick, R · 2017
Earlier work this paper cites.
The kinetics human action video dataset, 2017
Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., Suleyman, M., and Zisserman, A · 2017
Earlier work this paper cites.
Deeper, broader and artier domain generalization
Li, D., Yang, Y., Song, Y.-Z., and Hospedales, T. M · 2017
Earlier work this paper cites.
Feature pyramid networks for object detection
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S · 2017
Earlier work this paper cites.
Unified deep supervised domain adaptation and generalization
Motiian, S., Piccirilli, M., Adjeroh, D. A., and Doretto, G · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
Earlier work this paper cites.
Meta-learning with differentiable closed-form solvers
Bertinetto, L., Henriques, J. F., Torr, P. H., and Vedaldi, A · 2018
Earlier work this paper cites.
Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F., and Brendel, W · 2018
Earlier work this paper cites.
Invariant risk minimization
Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
Cited alongside, same era.
Why propensity scores should not be used for matching
King, G. and Nielsen, R · 2019
Cited alongside, same era.
The omniglot challenge: a 3-year progress report
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2019
Cited alongside, same era.
Detectron2
Wu, Y., Kirillov, A., Massa, F., Lo, W.-Y., and Girshick, R · 2019
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
Vicregl: Self-supervised learning of local visual features
Bardes, A., Ponce, J., and LeCun, Y · 2022
Later among the works it cites.
Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
Later among the works it cites.
Metaug: Contrastive learning via meta feature augmentation
Li, J., Qiang, W., Zheng, C., Su, B., and Xiong, H · 2022
Later among the works it cites.
Self-supervised learning via maximum entropy coding
Liu, X., Wang, Z., Li, Y.-L., and Wang, S · 2022
Later among the works it cites.
Interventional contrastive learning with meta semantic regularizer
Qiang, W., Li, J., Zheng, C., Su, B., and Xiong, H · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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 survey on contrastive self-supervised learning
Jaiswal, A., Babu, A. R., Zadeh, M. Z., Banerjee, D., and Makedon, F · 2020
Cited alongside, same era.
Variational autoencoders and nonlinear ica: A unifying framework
Khemakhem, I., Kingma, D., Monti, R., and Hyvarinen, A · 2020
Cited alongside, same era.
Contrastive multiview coding
Tian, Y., Krishnan, D., and Isola, P · 2020
Cited alongside, same era.
Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Wang, T. and Isola, P · 2020
Cited alongside, same era.
Adversarial domain adaptation with domain mixup
Xu, M., Zhang, J., Ni, B., Li, T., Wang, C., Tian, Q., and Zhang, W · 2020
Cited alongside, same era.
Tomasev, N., Bica, I., McWilliams, B., Buesing, L., Pascanu, R., Blundell, C., and Mitrovic, J · 2022
Later among the works it cites.
Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
Tong, Z., Song, Y., Wang, J., and Wang, L · 2022
Later among the works it cites.
Causal balancing for domain generalization
Wang, X., Saxon, M., Li, J., Zhang, H., Zhang, K., and Wang, W. Y · 2022
Later among the works it cites.
Volo: Vision outlooker for visual recognition
Yuan, L., Hou, Q., Jiang, Z., Feng, J., and Yan, S · 2022
Later among the works it cites.
On the effectiveness of out-of-distribution data in self-supervised long-tail learning
Bai, J., Liu, Z., Wang, H., Hao, J., Feng, Y., Chu, H., and Hu, H · 2023
Later among the works it cites.
Benchmarking self-supervised learning on diverse pathology datasets
Kang, M., Song, H., Park, S., Yoo, D., and Pereira, S · 2023
Later among the works it cites.
Meta attention-generation network for cross-granularity few-shot learning
Qiang, W., Li, J., Su, B., Fu, J., Xiong, H., and Wen, J.-R · 2023
Later among the works it cites.
Towards the sparseness of projection head in self-supervised learning
Song, Z., Su, X., Wang, J., Qiang, W., Zheng, C., and Sun, F · 2023
Later among the works it cites.
Evaluating and improving domain invariance in contrastive self-supervised learning by extrapolating the loss function
Zare, S. and Van Nguyen, H · 2023
Later among the works it cites.
Making self-supervised learning robust to spurious correlation via learning-speed aware sampling
Zhu, W., Liu, S., Fernandez-Granda, C., and Razavian, N · 2023
Later among the works it cites.
Self-supervised representation learning with meta comprehensive regularization
Guo, H., Ba, Y., Hu, J., Si, L., Qiang, W., and Shi, L · 2024
Later among the works it cites.
Views can be deceiving: Improved ssl through feature space augmentation
Hamidieh, K., Zhang, H., Sankaranarayanan, S., and Ghassemi, M · 2024
Later among the works it cites.
On the comparison between multi-modal and single-modal contrastive learning
Huang, W., Han, A., Chen, Y., Cao, Y., Xu, Z., and Suzuki, T · 2024
Later among the works it cites.
Self-supervised debiasing using low rank regularization
Park, G. Y., Jung, C., Lee, S., Ye, J. C., and Lee, S. W · 2024
Later among the works it cites.
On the generalization and causal explanation in self-supervised learning
Qiang, W., Song, Z., Gu, Z., Li, J., Zheng, C., Sun, F., and Xiong, H · 2024
Later among the works it cites.
On the discriminability of self-supervised representation learning
Song, Z., Qiang, W., Zheng, C., Sun, F., and Xiong, H · 2024
Later among the works it cites.
Self-guided masked autoencoders for domain-agnostic self-supervised learning
Xie, J., Lee, Y., Chen, A. S., and Finn, C · 2024
Later among the works it cites.