Hojin Jang

This study shows that training convolutional neural networks with blurry images improves their performance in predicting neural responses and aligns them better with human vision.

This study trains CNNs with blurry to clear images for face and object recognition, finding that face networks retain blur robustness while object networks do not, suggesting differences in processing strategies.

This study shows that noise-trained deep neural networks better match human performance and neural responses to noisy images than standard DNNs.