Search Results for author: Sangwoong Yoon

Found 8 papers, 3 papers with code

Generalized Contrastive Divergence: Joint Training of Energy-Based Model and Diffusion Model through Inverse Reinforcement Learning

no code implementations6 Dec 2023 Sangwoong Yoon, Dohyun Kwon, Himchan Hwang, Yung-Kyun Noh, Frank C. Park

We present Generalized Contrastive Divergence (GCD), a novel objective function for training an energy-based model (EBM) and a sampler simultaneously.

Evaluating Out-of-Distribution Detectors Through Adversarial Generation of Outliers

1 code implementation20 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.

Regularized Autoencoders for Isometric Representation Learning

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.

Information Retrieval Representation Learning +1

Adversarial Distributions Against Out-of-Distribution Detectors

no code implementations29 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.

Out of Distribution (OOD) Detection

Autoencoding Under Normalization Constraints

2 code implementations12 May 2021 Sangwoong Yoon, Yung-Kyun Noh, Frank Chongwoo Park

The specific role of the normalization constraint is to ensure that the out-of-distribution (OOD) regime has a small likelihood when samples are learned using maximum likelihood.

Outlier Detection

Spectrally Similar Graph Pooling

no code implementations1 Jan 2021 Kyoung-Woon On, Eun-Sol Kim, Il-Jae Kwon, Sangwoong Yoon, Byoung-Tak Zhang

To further investigate the effectiveness of our proposed method, we evaluate our approach on a real-world problem, image retrieval with visual scene graphs.

Image Retrieval Retrieval

Suppressing Outlier Reconstruction in Autoencoders for Out-of-Distribution Detection

no code implementations1 Jan 2021 Sangwoong Yoon, Yung-Kyun Noh, Frank C. Park

This phenomenon, which we refer to as outlier reconstruction, has a detrimental effect on the use of autoencoders for outlier detection, as an autoencoder will misclassify a clear outlier as being in-distribution.

Outlier Detection Out-of-Distribution Detection

Image-to-Image Retrieval by Learning Similarity between Scene Graphs

1 code implementation29 Dec 2020 Sangwoong Yoon, Woo Young Kang, Sungwook Jeon, SeongEun Lee, Changjin Han, Jonghun Park, Eun-Sol Kim

Based on this idea, we propose a novel approach for image-to-image retrieval using scene graph similarity measured by graph neural networks.

Graph Similarity Image Retrieval +3

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