3 citations · 8 across the 10 of their papers we have counts for
10 papers
Back to the Familiar Future: Failure Recovery for VLA Policies via Pre-Imagined Milestone Selection
Suyeon Shin, Juwon Kim, Hyeonbin Park +4
Vision-language-action (VLA) policies can deviate from nominal trajectories during manipulation, even when tasks remain physically feasible. Recovering from these deviations is cha…
Locality-aware Concept Bottleneck Model
Sujin Jeon, Hyundo Lee, Eungseo Kim +3
Concept bottleneck models (CBMs) are inherently interpretable models that make predictions based on human-understandable visual cues, referred to as concepts. As obtaining dense co…
Towards Spatially Consistent Image Generation: On Incorporating Intrinsic Scene Properties into Diffusion Models
Hyundo Lee, Suhyung Choi, Inwoo Hwang +1
Image generation models trained on large datasets can synthesize high-quality images but often produce spatially inconsistent and distorted images due to limited information about…
DUEL: Duplicate Elimination on Active Memory for Self-Supervised Class-Imbalanced Learning
Won-Seok Choi, Hyundo Lee, Dong-Sig Han +3
Recent machine learning algorithms have been developed using well-curated datasets, which often require substantial cost and resources. On the other hand, the direct use of raw dat…
L-SA: Learning Under-Explored Targets in Multi-Target Reinforcement Learning
Kibeom Kim, Hyundo Lee, Min Whoo Lee +3
Tasks that involve interaction with various targets are called multi-target tasks. When applying general reinforcement learning approaches for such tasks, certain targets that are…
Learning Geometry-aware Representations by Sketching
Hyundo Lee, Inwoo Hwang, Hyunsung Go +3
Understanding geometric concepts, such as distance and shape, is essential for understanding the real world and also for many vision tasks. To incorporate such information into a v…
DUEL: Adaptive Duplicate Elimination on Working Memory for Self-Supervised Learning
Won-Seok Choi, Dong-Sig Han, Hyundo Lee +2
In Self-Supervised Learning (SSL), it is known that frequent occurrences of the collision in which target data and its negative samples share the same class can decrease performanc…
Robust Imitation via Mirror Descent Inverse Reinforcement Learning
Dong-Sig Han, Hyunseo Kim, Hyundo Lee +2
Recently, adversarial imitation learning has shown a scalable reward acquisition method for inverse reinforcement learning (IRL) problems. However, estimated reward signals often b…
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning
Taehyeong Kim, Injune Hwang, Hyundo Lee +4
Active learning is widely used to reduce labeling effort and training time by repeatedly querying only the most beneficial samples from unlabeled data. In real-world problems where…
Encoder-Powered Generative Adversarial Networks
Jiseob Kim, Seungjae Jung, Hyundo Lee +1
We present an encoder-powered generative adversarial network (EncGAN) that is able to learn both the multi-manifold structure and the abstract features of data. Unlike the conventi…