6 citations · 7 across the 5 of their papers we have counts for
5 papers
GTA: Guided Transfer of Spatial Attention from Object-Centric Representations
SeokHyun Seo, Jinwoo Hong, JungWoo Chae +2
Utilizing well-trained representations in transfer learning often results in superior performance and faster convergence compared to training from scratch. However, even if such go…
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…
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…
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…
Semantic Noise Modeling for Better Representation Learning
Hyo-Eun Kim, Sangheum Hwang, Kyunghyun Cho
Latent representation learned from multi-layered neural networks via hierarchical feature abstraction enables recent success of deep learning. Under the deep learning framework, ge…