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Plos Computational Biology|September 23, 2025
Category-specific perceptual learning of robust object recognition modelled using deep neural networksHojin Jang, Frank TongJournal of Vision|November 12, 2021
Convolutional neural networks trained with a developmental sequence of blurry to clear images reveal core differences between face and object processingHojin Jang, Frank TongNature Communications|March 5, 2024
Improved modeling of human vision by incorporating robustness to blur in convolutional neural networksHojin Jang, Frank TongAnnual Review of Vision Science|June 22, 2026
Ecological Vision Hypothesis: Training Deep Neural Networks for Robustness and Human AlignmentFrank Tong, Hojin JangBiorxiv : the Preprint Server for Biology|August 14, 2023
Improved modeling of human vision by incorporating robustness to blur in convolutional neural networksHojin Jang, Frank TongPlos Biology|December 9, 2021
Noise-trained deep neural networks effectively predict human vision and its neural responses to challenging imagesHojin Jang, Devin McCormack, Frank TongCommunications Biology|March 7, 2025
Configural processing as an optimized strategy for robust object recognition in neural networksHojin Jang, Pawan Sinha, Xavier BoixJournal of Neuroscience Methods|October 19, 2019
Test-retest reliability of spatial patterns from resting-state functional MRI using the restricted Boltzmann machine and hierarchically organized spatial patterns from the deep belief networkHyun-Chul Kim, Hojin Jang, Jong-Hwan LeeNeuroimage|April 16, 2016
Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasksHojin Jang, Sergey M Plis, Vince D Calhoun, et al.Pageof 10