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Learning representations with self-supervision for convolutional networks (CNN) has been validated to be effective for vision tasks.
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Y. Liu, K. Chen, C. Liu, Z. Qin, Z. Luo, and J. Wang, “Structured knowledge distillation for semantic segmentation,” in IEEE Conf. Comput. Vis. Pattern Recog. , June 2019
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T. He, C. Shen, Z. Tian, D. Gong, C. Sun, and Y. Yan, “Knowledge adaptation for efficient semantic segmentation,” in IEEE Conf. Comput. Vis. Pattern Recog. , June 2019
2019
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I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in Int. Conf. Learn. Represent. , 2019
2019
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M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin, “Unsupervised learning of visual features by contrasting cluster assignments,” in Adv. Neural Inform. Process. Syst. , 2020
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2021
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2021
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2020
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J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. Ávila Pires, Z. Guo, M. G. Azar, B. Piot, K. Kavukcuoglu, R. Munos, and M. Valko, “Bootstrap your own latent - a new approach to self-supervised learning,” in Adv. Neural Inform. Process. Syst. , 2020
2020
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K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in IEEE Conf. Comput. Vis. Pattern Recog. , June 2020
2020
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T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International Conference on Machine Learning (ICML) , 2020
2020
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X. Zhan, J. Xie, Z. Liu, Y.-S. Ong, and C. C. Loy, “Online deep clustering for unsupervised representation learning,” in IEEE Conf. Comput. Vis. Pattern Recog. , June 2020
2020
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A. YM., R. C., and V. A., “Self-labelling via simultaneous clustering and representation learning,” in Int. Conf. Learn. Represent. , 2020
2020
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Y. Tian, X. Chen, and S. Ganguli, “Understanding self-supervised learning dynamics without contrastive pairs,” in International Conference on Machine Learning (ICML) , 2020
2020
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N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in Eur. Conf. Comput. Vis. Springer, 2020, pp. 213–229
2020
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H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou, “Training data-efficient image transformers & distillation through attention,” in International Conference on Machine Learning (ICML) . PMLR, 2021, pp. 10 347–10 357
2021
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Z. Li, Z. Chen, F. Yang, W. Li, Y. Zhu, C. Zhao, R. Deng, L. Wu, R. Zhao, M. Tang, and J. Wang, “MST: Masked self-supervised transformer for visual representation,” in Adv. Neural Inform. Process. Syst. , 2021
2021
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2021
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2021
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O. Siméoni, G. Puy, H. V. Vo, S. Roburin, S. Gidaris, A. Bursuc, P. Pérez, R. Marlet, and J. Ponce, “Localizing objects with self-supervised transformers and no labels,” in Brit. Mach. Vis. Conf. , November 2021
2021
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M. Ki, Y. Uh, J. Choe, and H. Byun, “Contrastive attention maps for self-supervised co-localization,” in Int. Conf. Comput. Vis. , October 2021, pp. 2803–2812
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2021
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L. Liu, Q. Huang, S. Lin, H. Xie, B. Wang, X. Chang, and X. Liang, “Exploring inter-channel correlation for diversity-preserved knowledge distillation,” in Int. Conf. Comput. Vis. , October 2021, pp. 8271–8280
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M. Patrick, Y. M. Asano, P. Kuznetsova, R. Fong, J. a. F. Henriques, G. Zweig, and A. Vedaldi, “On compositions of transformations in contrastive self-supervised learning,” in Int. Conf. Comput. Vis. , 2021
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S. Gao, Z.-Y. Li, Q. Han, M.-M. Cheng, and L. Wang, “Rf-next: Efficient receptive field search for convolutional neural networks,” IEEE Trans. Pattern Anal. Mach. Intell. , 2022
2022
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K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in IEEE Conf. Comput. Vis. Pattern Recog. , June 2022, pp. 16 000–16 009
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J. Zhou, C. Wei, H. Wang, W. Shen, C. Xie, A. Yuille, and T. Kong, “ibot: Image bert pre-training with online tokenizer,” Int. Conf. Learn. Represent. , 2022
2022
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Y.-H. Wu, Y. Liu, X. Zhan, and M.-M. Cheng, “P2T: Pyramid pooling transformer for scene understanding,” IEEE Trans. Pattern Anal. Mach. Intell. , 2022
2022
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C. Li, J. Yang, P. Zhang, M. Gao, B. Xiao, X. Dai, L. Yuan, and J. Gao, “Efficient self-supervised vision transformers for representation learning,” in Int. Conf. Learn. Represent. , 2022
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2022
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H. Bao, L. Dong, S. Piao, and F. Wei, “BEit: BERT pre-training of image transformers,” in Int. Conf. Learn. Represent. , 2022
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Z. Xie, Z. Zhang, Y. Cao, Y. Lin, J. Bao, Z. Yao, Q. Dai, and H. Hu, “Simmim: A simple framework for masked image modeling,” in IEEE Conf. Comput. Vis. Pattern Recog. , June 2022
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2022
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C. Yang, H. Zhou, Z. An, X. Jiang, Y. Xu, and Q. Zhang, “Cross-image relational knowledge distillation for semantic segmentation,” in IEEE Conf. Comput. Vis. Pattern Recog. , June 2022, pp. 12 319–12 328
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M. Hamilton, Z. Zhang, B. Hariharan, N. Snavely, and W. T. Freeman, “Unsupervised semantic segmentation by distilling feature correspondences,” in Int. Conf. Learn. Represent. , 2022
2022
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Y. Wang, X. Shen, S. X. Hu, Y. Yuan, J. L. Crowley, and D. Vaufreydaz, “Self-supervised transformers for unsupervised object discovery using normalized cut,” in IEEE Conf. Comput. Vis. Pattern Recog. , June 2022
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2022
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A. Bardes, J. Ponce, and Y. LeCun, “VICReg: Variance-invariance-covariance regularization for self-supervised learning,” in Int. Conf. Learn. Represent. , 2022
2022
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L. Ericsson, H. Gouk, and T. M. Hospedales, “Why do self-supervised models transfer? investigating the impact of invariance on downstream tasks,” 2022
2022
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H. SUN and M. LI, “Enhancing unsupervised domain adaptation by exploiting the conceptual consistency of multiple self-supervised tasks,” SCIENCE CHINA Information Sciences , vol. 66, no. 4, pp. 142 101–, 2023
2023
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H. Lu, Y. Huo, M. Ding, N. Fei, and Z. Lu, “Cross-modal contrastive learning for generalizable and efficient image-text retrieval,” Machine Intelligence Research , pp. 1–14, 2023
2023
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W.-C. Wang, E. Ahn, D. Feng, and J. Kim, “A review of predictive and contrastive self-supervised learning for medical images,” Machine Intelligence Research , pp. 483–513, 2023
2023
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L. Wang, H. Xu, and W. Kang, “Mvcontrast: Unsupervised pretraining for multi-view 3d object recognition,” Machine Intelligence Research , pp. 1–12, 2023
2023
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