Early experience with low-pass filtered images facilitates visual category learning in a neural network model
收藏DataCite Commons2023-08-17 更新2024-07-13 收录
下载链接：
https://kilthub.cmu.edu/articles/dataset/Early_experience_with_low-pass_filtered_images_facilitates_visual_category_learning_in_a_neural_network_model/23972115/1
下载链接
链接失效反馈官方服务：
资源简介：
Dataset consisting of neural network models trained on low-pass filtered and intact images. <br> Related to our paper: Jinsi* O, Henderson* MM, Tarr MJ (2023) Early experience with low-pass filtered images facilitates visual category learning in a neural network model. PLoS ONE 18(1): e0280145. https://doi.org/10.1371/journal.pone.0280145 <br> Each .tar file contains .csv and .pt files resulting from one method of model training (grayscale or colored images, training from-scratch on images from ecoset or fine-tuning models on imagenet). Numbered folders correspond to models initialized with different random seeds. Different files in each folder correspond to different blur conditions. See paper for more details. <br> For all experiment code, see our github repository at: https://github.com/tarrlab/startingblurry <br> Contact mmhender@cmu.edu or mt01@andrew.cmu.edu with any questions or concerns.
提供机构：
Carnegie Mellon University
创建时间：
2023-08-17
