Orthogonalized SGD and Nested Architectures for Anytime Neural Networks

• URL: http://arxiv.org/abs/2008.06635v1
• Date: Sat, 15 Aug 2020 03:06:34 GMT
• Title: Orthogonalized SGD and Nested Architectures for Anytime Neural Networks
• Authors: Chengcheng Wan, Henry Hoffmann, Shan Lu, Michael Maire
• Abstract summary: Orthogonalized SGD dynamically re-balances task-specific gradients when training a multitask network. Experiments demonstrate that training with Orthogonalized SGD significantly improves accuracy of anytime networks.
• Score: 30.598394152055338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel variant of SGD customized for training network architectures that support anytime behavior: such networks produce a series of increasingly accurate outputs over time. Efficient architectural designs for these networks focus on re-using internal state; subnetworks must produce representations relevant for both immediate prediction as well as refinement by subsequent network stages. We consider traditional branched networks as well as a new class of recursively nested networks. Our new optimizer, Orthogonalized SGD, dynamically re-balances task-specific gradients when training a multitask network. In the context of anytime architectures, this optimizer projects gradients from later outputs onto a parameter subspace that does not interfere with those from earlier outputs. Experiments demonstrate that training with Orthogonalized SGD significantly improves generalization accuracy of anytime networks.

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