1 code implementation • 26 Oct 2023 • Yonghyeon LEE
Motion Manifold Primitives (MMP), a manifold-based approach for encoding basic motion skills, can produce diverse trajectories, enabling the system to adapt to unseen constraints.
no code implementations • 19 Sep 2023 • Yonghyeon LEE, Frank Chongwoo Park
We propose a family of curvature-based regularization terms for deep generative model learning.
no code implementations • 15 Sep 2023 • Yonghyeon LEE
Given a set of high-dimensional data points that approximately lie on some lower-dimensional manifold, an autoencoder learns the \textit{manifold} and its \textit{coordinate chart}, simultaneously.
1 code implementation • 20 Aug 2022 • Sangwoong Yoon, Jinwon Choi, Yonghyeon LEE, Yung-Kyun Noh, Frank Chongwoo Park
A reliable evaluation method is essential for building a robust out-of-distribution (OOD) detector.
1 code implementation • NeurIPS 2021 • Yonghyeon LEE, Hyeokjun Kwon, Frank Park
Unlike existing graph-based methods that attempt to encode the training data to some prescribed latent space distribution -- one consequence being that only the encoder is the object of the regularization -- NRAE merges local connectivity information contained in the neighborhood graphs with local quadratic approximations of the decoder function to formulate a new neighborhood reconstruction loss.
no code implementations • 29 Sep 2021 • Yonghyeon LEE, Seungyeon Kim, Jinwon Choi, Frank C. Park
The only requirement on the part of the user is the choice of a meaningful underlying probability distribution, which is more intuitive and natural to make than what is required in existing ad hoc formulations.
no code implementations • ICLR 2022 • Yonghyeon LEE, Sangwoong Yoon, MinJun Son, Frank C. Park
The recent success of autoencoders for representation learning can be traced in large part to the addition of a regularization term.
no code implementations • 29 Sep 2021 • Sangwoong Yoon, Jinwon Choi, Yonghyeon LEE, Yung-Kyun Noh, Frank C. Park
As an outlier may deviate from the training distribution in unexpected ways, an ideal OOD detector should be able to detect all types of outliers.