Do Deep Neural Networks Suffer from Crowding?
Crowding is a visual effect suffered by humans, in which an object that can be recognized in isolation can no longer be recognized when other objects, called flankers, are placed close to it. In this work, we study the effect of crowding in artificial Deep Neural Networks for object recognition. We...
Main Authors: | Volokitin, Anna, Roig, Gemma, Poggio, Tomaso |
---|---|
Format: | Technical Report |
Language: | en_US |
Published: |
Center for Brains, Minds and Machines (CBMM), arXiv
2017
|
Subjects: | |
Online Access: | http://hdl.handle.net/1721.1/110348 |
Similar Items
-
Do deep neural networks suffer from crowding?
by: Volokitin, Anna, et al.
Published: (2022) -
Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?
by: Poggio, Tomaso, et al.
Published: (2016) -
Deep vs. shallow networks : An approximation theory perspective
by: Mhaskar, Hrushikesh, et al.
Published: (2016) -
Foveation-based Mechanisms Alleviate Adversarial Examples
by: Lou, Yan, et al.
Published: (2016) -
Deep neural network-based bandwidth enhancement of photoacoustic data
by: Gutta, Sreedevi, et al.
Published: (2017)