14 citations · 33 across the 11 of their papers we have counts for
7 papers · 1 filter
Test-time Fourier Style Calibration for Domain Generalization
Xingchen Zhao, Chang Liu, Anthony Sicilia +2
The topic of generalizing machine learning models learned on a collection of source domains to unknown target domains is challenging. While many domain generalization (DG) methods…
Point Cloud Augmentation with Weighted Local Transformations
Sihyeon Kim, Sanghyeok Lee, Dasol Hwang +3
Despite the extensive usage of point clouds in 3D vision, relatively limited data are available for training deep neural networks. Although data augmentation is a standard approach…
Multi-Domain Learning by Meta-Learning: Taking Optimal Steps in Multi-Domain Loss Landscapes by Inner-Loop Learning
Anthony Sicilia, Xingchen Zhao, Davneet Minhas +5
We consider a model-agnostic solution to the problem of Multi-Domain Learning (MDL) for multi-modal applications. Many existing MDL techniques are model-dependent solutions which e…
Robust White Matter Hyperintensity Segmentation on Unseen Domain
Xingchen Zhao, Anthony Sicilia, Davneet Minhas +5
Typical machine learning frameworks heavily rely on an underlying assumption that training and test data follow the same distribution. In medical imaging which increasingly begun a…
Online Graph Completion: Multivariate Signal Recovery in Computer Vision
Won Hwa Kim, Mona Jalal, Seongjae Hwang +2
The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e.g., human supervision)…
Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders
Xin Ma, Guorong Wu, Seong Jae Hwang +1
Tremendous recent literature show that associations between different brain regions, i.e., brain connectivity, provide early symptoms of neurological disorders. Despite significant…
Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging
Seong Jae Hwang, Zirui Tao, Won Hwa Kim +1
Generative models using neural network have opened a door to large-scale studies for various application domains, especially for studies that suffer from lack of real samples to ob…