March 13, 2024, 4:42 a.m. | Linrui Dai, Rongzhao Zhang, Zhongzhen Huang, Xiaofan Zhang

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.07247v1 Announce Type: cross
Abstract: The annotation burden and extensive labor for gathering a large medical dataset with images and corresponding labels are rarely cost-effective and highly intimidating. This results in a lack of abundant training data that undermines downstream tasks and partially contributes to the challenge image analysis faces in the medical field. As a workaround, given the recent success of generative neural models, it is now possible to synthesize image datasets at a high fidelity guided by external …

arxiv cs.cv cs.lg eess.iv framework text type

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