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When Neural Networks Using Different Sensors Create Similar Features

2021-11-04 10:35:57
Hugues Moreau (CEA-LETI, LIRIS), Andréa Vassilev (CEA-LETI), Liming Chen (LIRIS, ECL)
arXiv_CV
     
Abstract

Multimodal problems are omnipresent in the real world: autonomous driving, robotic grasping, scene understanding, etc... We draw from the well-developed analysis of similarity to provide an example of a problem where neural networks are trained from different sensors, and where the features extracted from these sensors still carry similar information. More precisely, we demonstrate that for each sensor, the linear combination of the features from the last layer that correlates the most with other sensors corresponds to the classification components of the classification layer.

Abstract (translated)

URL

https://arxiv.org/abs/2111.02732

PDF

https://arxiv.org/pdf/2111.02732.pdf

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