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Early experience with low-pass filtered images facilitates visual category learning in a neural network model

dataset
posted on 2023-08-17, 15:00 authored by Margaret HendersonMargaret Henderson, Michael TarrMichael Tarr

Dataset consisting of neural network models trained on low-pass filtered and intact images. 


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


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.


For all experiment code, see our github repository at: https://github.com/tarrlab/startingblurry


Contact mmhender@cmu.edu or mt01@andrew.cmu.edu with any questions or concerns.

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Publisher Statement

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

Date

2023-08-16

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