121 citations · 181 across the 3 of their papers we have counts for
3 papers
Improving neural network representations using human similarity judgments
Lukas Muttenthaler, Lorenz Linhardt, Jonas Dippel +4
Deep neural networks have reached human-level performance on many computer vision tasks. However, the objectives used to train these networks enforce only that similar images are e…
What shapes feature representations? Exploring datasets, architectures, and training
Katherine L. Hermann, Andrew K. Lampinen
In naturalistic learning problems, a model's input contains a wide range of features, some useful for the task at hand, and others not. Of the useful features, which ones does the…
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks
Katherine L. Hermann, Ting Chen, Simon Kornblith
Recent work has indicated that, unlike humans, ImageNet-trained CNNs tend to classify images by texture rather than by shape. How pervasive is this bias, and where does it come fro…