Date of Award

6-1-2014

Document Type

Thesis (Undergraduate)

Department

Department of Computer Science

First Advisor

Lorenzo Torresani

Abstract

In this paper, we propose to regularize deep neural nets with a new type of multitask learning where the auxiliary task is formed by agglomerating classes into super-classes. As such, it is possible to jointly train the network on the class-based classification problem AND super-class based classification problem. We study this in settings where the training set is small and show that , concurrently with a regularization scheme of randomly reinitializing weights in deeper layers, this leads to competitive results on the ImageNet and Caltech-256 datasets and state-of-the-art results on CIFAR-100.

Comments

Originally posted in the Dartmouth College Computer Science Technical Report Series, number TR2014-762.

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