Decoding the Deep: Exploring Class Hierarchies of Deep Representations Using Multiresolution Matrix Factorization

Vamsi K. Ithapu; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 45-54

Abstract


The necessity of depth has led to a family of designs referred to as very deep networks (e.g., GoogLeNet has 22 layers). As the depth increases even further, the need for appropriate tools to explore the space of hidden representations becomes paramount. Classical PCA or eigen-spectrum based global approaches do not model the complex inter-class relationships. In this work, we propose a novel decomposition referred to as multiresolution matrix factorization that models hierarchical and compositional structure in symmetric matrices. This new decomposition efficiently infers semantic relationships among deep representations, even when they are not explicitly trained to do so. We show that it is a valuable tool in understanding the landscape of hidden representations, in adapting existing architectures for new tasks and also for designing new architectures using interpretable, human-releatable, class-by-class relationships that we hope the network to learn.

Related Material


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[bibtex]
@InProceedings{Ithapu_2017_CVPR_Workshops,
author = {Ithapu, Vamsi K.},
title = {Decoding the Deep: Exploring Class Hierarchies of Deep Representations Using Multiresolution Matrix Factorization},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}