Deep Networks with Fast Retraining

• URL: http://arxiv.org/abs/2008.07387v2
• Date: Mon, 4 Jan 2021 23:37:54 GMT
• Title: Deep Networks with Fast Retraining
• Authors: Wandong Zhang (1 and 2), Yimin Yang (2 and 3), Jonathan Wu (1) ((1) University of Windsor, (2) Lakehead University, (3) Vector Institute for Artificial Intelligence)
• Abstract summary: This paper proposes a novel MP inverse-based fast retraining strategy for deep convolutional neural network (DCNN) learning. In each training, a random learning strategy that controls the number of convolutional layers trained in the backward pass is first utilized. Then, an MP inverse-based batch-by-batch learning strategy, which enables the network to be implemented without access to industrial-scale computational resources, is developed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent work [1] has utilized Moore-Penrose (MP) inverse in deep convolutional neural network (DCNN) learning, which achieves better generalization performance over the DCNN with a stochastic gradient descent (SGD) pipeline. However, Yang's work has not gained much popularity in practice due to its high sensitivity of hyper-parameters and stringent demands of computational resources. To enhance its applicability, this paper proposes a novel MP inverse-based fast retraining strategy. In each training epoch, a random learning strategy that controls the number of convolutional layers trained in the backward pass is first utilized. Then, an MP inverse-based batch-by-batch learning strategy, which enables the network to be implemented without access to industrial-scale computational resources, is developed to refine the dense layer parameters. Experimental results empirically demonstrate that fast retraining is a unified strategy that can be used for all DCNNs. Compared to other learning strategies, the proposed learning pipeline has robustness against the hyper-parameters, and the requirement of computational resources is significantly reduced. [1] Y. Yang, J. Wu, X. Feng, and A. Thangarajah, "Recomputation of dense layers for the perfor-238mance improvement of dcnn," IEEE Trans. Pattern Anal. Mach. Intell., 2019.

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