331 citations · 462 across the 14 of their papers we have counts for
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cs.CV2023★ 1 cited
Few-shot Fine-tuning is All You Need for Source-free Domain Adaptation
Suho Lee, Seungwon Seo, Jihyo Kim +2
Recently, source-free unsupervised domain adaptation (SFUDA) has emerged as a more practical and feasible approach compared to unsupervised domain adaptation (UDA) which assumes th…
cs.CV2023★ 6 cited
Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning
Jae-Hun Lee, Doyoung Yoon, ByeongMoon Ji +2
Linear probing (LP) (and -NN) on the upstream dataset with labels (e.g., ImageNet) and transfer learning (TL) to various downstream datasets are commonly employed to evaluate th…
cs.CV2023
Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions
Jihyo Kim, Jeonghyeon Kim, Sangheum Hwang
Active learning aims to identify the most informative data from an unlabeled data pool that enables a model to reach the desired accuracy rapidly. This benefits especially deep neu…