Sharing cutting-edge global technological trends, presenting research achievements, and fostering industry-academia collaboration, UNIST Graduate School of Artificial Intelligence hosted the ‘2025 UNIST AI Technology Open Workshop... Read More
Abstract Flat minima, known to enhance generalization and robustness in supervised learning, remain largely unexplored in generative models. In this work, we systematically investigate the role of loss surface flatness in generative models,... Read More
Abstract Understanding 3D scenes semantically and spatially is crucial for the safe navigation of robots and autonomous vehicles, aiding obstacle avoidance and accurate trajectory planning. Camera-based 3D semantic occupancy prediction, whi... Read More
UNIST has been selected to participate in the ‘AI Star Fellowship‘ project, a key initiative supported by the Ministry of Science and ICT (MSIT) and the Institute for Info & communication Tech Planning & evaluation (IITP... Read More
Abstract Dynamic MRI reconstruction, one of inverse problems, has seen a surge by the use of deep learning techniques. Especially, the practical difficulty of obtaining ground truth data has led to the emergence of unsupervised learning app... Read More
Abstract Meta reinforcement learning aims to develop policies that generalize to unseen tasks sampled from a task distribution. While context-based meta-RL methods improve task representation using task latents, they often struggle with out... Read More
Abstract Traditional robust methods in multi-agent reinforcement learning (MARL) often struggle against coordinated adversarial attacks in cooperative scenarios. To address this limitation, we propose the Wolfpack Adversarial Attack framewo... Read More
Abstract We tackle animatable 3D dog reconstruction from a single image, noting the overlooked potential of animals. Particularly, we focus on dogs, emphasizing their intrinsic characteristics that complicate 3D observation. First, the cons... Read More
Abstract Learning with multiple modalities has recently demonstrated significant gains in many domains by maximizing the shared information across modalities. However, the current approaches strongly rely on high-quality paired datasets, wh... Read More
Abstract Investigating flat minima on loss surfaces in parameter space is well-documented in the supervised learning context, highlighting its advantages for model generalization. However, limited attention has been paid to the reinforcemen... Read More
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