Fixing the problems of deep neural networks will require better training data and learning algorithms
Computer Vision and Pattern Recognition
2023-11-23 v1 Artificial Intelligence
Machine Learning
Neurons and Cognition
Abstract
Bowers and colleagues argue that DNNs are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those of humans. We show that this problem is worsening as DNNs are becoming larger-scale and increasingly more accurate, and prescribe methods for building DNNs that can reliably model biological vision.
Cite
@article{arxiv.2311.12819,
title = {Fixing the problems of deep neural networks will require better training data and learning algorithms},
author = {Drew Linsley and Thomas Serre},
journal= {arXiv preprint arXiv:2311.12819},
year = {2023}
}
Comments
Published as a commentary in Behavioral and Brain Sciences