Regularization of deep neural network with batch contrastive loss
Neural networks have become deeper in recent years and this has improved its capacity to handle more complex tasks. However, deep neural network has more parameters and is easier to overfit, especially when training samples are insufficient. In this paper, we present a new regularization technique c...
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Main Authors: | , , , , |
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Format: | Article |
Published: |
Institute of Electrical and Electronics Engineers
2021
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Subjects: | |
Online Access: | http://eprints.um.edu.my/28105/ |
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Institution: | Universiti Malaya |