BCN: Batch Channel Normalization
Keywords: Normalization technique, batch normalization, batch channel normalization
TL;DR: We propose a simple normalization technique called Batch Channel Normalization (BCN) that separately normalizes inputs along the (N, H, W) and (C, H, W) axes, then combine the normalized outputs based on adaptive parameters.
Abstract: Normalization techniques enable higher learning rates and are less careful in initialization. Unlike the standard Batch Normalization (BN) and Layer Normalization (LN), where BN computes the mean and variance along the (N, H, W) axes (N is the batch axes, H and W are the spatial height and width axes) and LN computes the mean and variance along the (C, H, W) axes (C is the channel axes), this paper presents a simple normalization technique called Batch Channel Normalization (BCN). BCN separately normalizes inputs along the (N, H, W) and (C, H, W) axes, then combine the normalized outputs based on adaptive parameters. BCN exploits both the channel and batch dependence and adaptively combines the advantages of BN and LN. As a basic block, BCN can be easily integrated into existing models for various applications in the field of computer vision. Empirical results show that the proposed (BCN) technique improves the
generalization performance of various models.
Primary Area: general machine learning (i.e., none of the above)
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Submission Number: 4389
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