publication . Preprint . 2017

Quantifying Translation-Invariance in Convolutional Neural Networks

Kauderer-Abrams, Eric;
Open Access English
  • Published: 10 Dec 2017
Abstract
A fundamental problem in object recognition is the development of image representations that are invariant to common transformations such as translation, rotation, and small deformations. There are multiple hypotheses regarding the source of translation invariance in CNNs. One idea is that translation invariance is due to the increasing receptive field size of neurons in successive convolution layers. Another possibility is that invariance is due to the pooling operation. We develop a simple a tool, the translation-sensitivity map, which we use to visualize and quantify the translation-invariance of various architectures. We obtain the surprising result that arc...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
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