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• W4386076260 abstract "Deep learning in general domains has constantly been extended to domain-specific tasks requiring the recognition of fine-grained characteristics. However, real-world applications for fine-grained tasks suffer from two challenges: a high reliance on expert knowledge for annotation and necessity of a versatile model for various downstream tasks in a specific domain (e.g., prediction of categories, bounding boxes, or pixel-wise annotations). Fortunately, the recent self-supervised learning (SSL) is a promising approach to pretrain a model without annotations, serving as an effective initialization for any downstream tasks. Since SSL does not rely on the presence of annotation, in general, it utilizes the large-scale unlabeled dataset, referred to as an open-set. In this sense, we introduce a novel Open-Set Self-Supervised Learning problem under the assumption that a large-scale unlabeled open-set is available, as well as the fine-grained target dataset, during a pretraining phase. In our problem setup, it is crucial to consider the distribution mismatch between the open-set and target dataset. Hence, we propose SimCore algorithm to sample a coreset, the subset of an open-set that has a minimum distance to the target dataset in the latent space. We demonstrate that SimCore significantly improves representation learning performance through extensive experimental settings, including eleven fine-grained datasets and seven open-sets in various downstream tasks." @default.
• W4386076260 created "2023-08-23" @default.
• W4386076260 creator A5056038923 @default.
• W4386076260 creator A5061665831 @default.
• W4386076260 creator A5091674853 @default.
• W4386076260 date "2023-06-01" @default.
• W4386076260 modified "2023-09-27" @default.
• W4386076260 title "Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learning" @default.
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• W4386076260 doi "https://doi.org/10.1109/cvpr52729.2023.00728" @default.
• W4386076260 hasPublicationYear "2023" @default.
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