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Shared Roots: Regularizing Deep Neural Networks through Multitask Learning
Piotr Teterwak, Lorenzo Torresani
Dartmouth TR2014-762

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.

Note:

Senior Honors Thesis. Advisor: Lorenzo Torresani.


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   Piotr Teterwak and Lorenzo Torresani, "Shared Roots: Regularizing Deep Neural Networks through Multitask Learning." Dartmouth Computer Science Technical Report TR2014-762, June 2014.


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