The role of textural statistics vs. outer contours in deep CNN and neural responses to objects
By Bria L Long, Talia Konkle. 2018
10 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Matthias Bethge
- Robert Geirhos
- Wieland Brendel
- Felix Wichmann
- Claudio Michaelis
- Patrick Esser
- Axel Sauer
- Ekin Dogus Cubuk
In Computer Vision and Pattern Recognition
- Wieland Brendel
- Matthias Bethge
- Robert Geirhos
- Felix Wichmann
- Claudio Michaelis
- Luc Van Gool
- Trevor Darrell
- Bernt Schiele
Citations in the corpus
- ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness (2018, background): “…CNNs match texture appearance for humans (Wallis et al., 2017), and their predictive power for neural responses along the human ventral stream appears to be largely due to human-like texture representations, but not human-like contour representations (Laskar et al., 2018; Long & Konkle, 2018).”