Search Results for author: Taewon Kim

Found 3 papers, 2 papers with code

NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation

1 code implementation10 Aug 2022 Taesik Gong, Jongheon Jeong, Taewon Kim, Yewon Kim, Jinwoo Shin, Sung-Ju Lee

Test-time adaptation (TTA) is an emerging paradigm that addresses distributional shifts between training and testing phases without additional data acquisition or labeling cost; only unlabeled test data streams are used for continual model adaptation.

Autonomous Driving Test-time Adaptation

Acceleration of Actor-Critic Deep Reinforcement Learning for Visual Grasping in Clutter by State Representation Learning Based on Disentanglement of a Raw Input Image

no code implementations27 Feb 2020 Taewon Kim, Yeseong Park, Youngbin Park, Il Hong Suh

For a robotic grasping task in which diverse unseen target objects exist in a cluttered environment, some deep learning-based methods have achieved state-of-the-art results using visual input directly.

Disentanglement Reinforcement Learning (RL) +1

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