Original title: Fixing the problems of deep neural networks will require better training data and learning algorithms
Authors: Drew Linsley, Thomas Serre
In their article, Bowers and team highlight a critical issue: deep neural networks (DNNs) don’t mirror human vision well. These networks achieve high accuracy differently from human strategies, a divergence growing more pronounced as DNNs expand in scale and accuracy. The focus is on proposing remedies to create DNNs that faithfully represent biological vision. This study underlines the urgency to address the disparity between DNNs and human vision, offering methods to develop networks that better align with how humans perceive visual information. By recognizing the limitations of current DNNs, the article sets a path toward crafting models that more accurately reflect the intricacies of human vision.
Original article: https://arxiv.org/abs/2311.12819