MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning

• URL: http://arxiv.org/abs/2011.12511v2
• Date: Sat, 28 Nov 2020 16:29:36 GMT
• Title: MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning
• Authors: Sen Lin, Li Yang, Zhezhi He, Deliang Fan, Junshan Zhang
• Abstract summary: We propose a holistic approach to jointly train the backbone network and the channel gating. We develop a federated meta-learning approach to jointly learn good meta-initializations for both backbone networks and gating modules.
• Score: 46.79356071007187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While deep learning has achieved phenomenal successes in many AI applications, its enormous model size and intensive computation requirements pose a formidable challenge to the deployment in resource-limited nodes. There has recently been an increasing interest in computationally-efficient learning methods, e.g., quantization, pruning and channel gating. However, most existing techniques cannot adapt to different tasks quickly. In this work, we advocate a holistic approach to jointly train the backbone network and the channel gating which enables dynamical selection of a subset of filters for more efficient local computation given the data input. Particularly, we develop a federated meta-learning approach to jointly learn good meta-initializations for both backbone networks and gating modules, by making use of the model similarity across learning tasks on different nodes. In this way, the learnt meta-gating module effectively captures the important filters of a good meta-backbone network, based on which a task-specific conditional channel gated network can be quickly adapted, i.e., through one-step gradient descent, from the meta-initializations in a two-stage procedure using new samples of that task. The convergence of the proposed federated meta-learning algorithm is established under mild conditions. Experimental results corroborate the effectiveness of our method in comparison to related work.

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