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Self-Supervised Contrastive Learning has proven effective in deriving high-quality representations from unlabeled data.
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Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 15750–15758 (2021)
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Ge, S., Mishra, S., Li, C.L., Wang, H., Jacobs, D.: Robust contrastive learning using negative samples with diminished semantics. Advances in Neural Information Processing Systems 34
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Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., Tang, J.: Self-supervised learning: Generative or contrastive. IEEE transactions on knowledge and data engineering 35
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
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Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I.: Zero-shot text-to-image generation. In: International Conference on Machine Learning. pp. 8821–8831. PMLR (2021)
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Robinson, J., Sun, L., Yu, K., Batmanghelich, K., Jegelka, S., Sra, S.: Can contrastive learning avoid shortcut solutions? Advances in neural information processing systems 34
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Tian, Y., Henaff, O.J., Van den Oord, A.: Divide and contrast: Self-supervised learning from uncurated data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10063–10074 (2021)
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Van Gansbeke, W., Vandenhende, S., Georgoulis, S., Van Gool, L.: Unsupervised semantic segmentation by contrasting object mask proposals. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10052–10062 (2021)
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2022
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Bleeker, M., Yates, A., de Rijke, M.: Reducing predictive feature suppression in resource-constrained contrastive image-caption retrieval. Transactions on Machine Learning Research (2023)
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2023
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2023
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2023
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Liu, F., Lin, K., Li, L., Wang, J., Yacoob, Y., Wang, L.: Mitigating hallucination in large multi-modal models via robust instruction tuning. In: The Twelfth International Conference on Learning Representations (2023)
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2023
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2023
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Lan, X., Yan, H., Hong, S., Feng, M.: Towards enhancing time series contrastive learning: A dynamic bad pair mining approach. In: The Twelfth International Conference on Learning Representations (2024), https://openreview.net/forum?id=K2c04ulKXn
2024
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2024
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