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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.
Senior Honors Thesis. Advisor: Lorenzo Torresani.
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
Or copy and paste:
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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