The Level Weighted Structural Similarity Loss: A Step Away from MSE

Authors

  • Yingjing Lu Carnegie Mellon University

DOI:

https://doi.org/10.1609/aaai.v33i01.33019989

Abstract

The Mean Square Error (MSE) has shown its strength when applied in deep generative models such as Auto-Encoders to model reconstruction loss. However, in image domain especially, the limitation of MSE is obvious: it assumes pixel independence and ignores spatial relationships of samples. This contradicts most architectures of Auto-Encoders which use convolutional layers to extract spatial dependent features. We base on the structural similarity metric (SSIM) and propose a novel level weighted structural similarity (LWSSIM) loss for convolutional Auto-Encoders. Experiments on common datasets on various Auto-Encoder variants show that our loss is able to outperform the MSE loss and the Vanilla SSIM loss. We also provide reasons why our model is able to succeed in cases where the standard SSIM loss fails.

Downloads

Published

2019-07-17

How to Cite

Lu, Y. (2019). The Level Weighted Structural Similarity Loss: A Step Away from MSE. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9989-9990. https://doi.org/10.1609/aaai.v33i01.33019989

Issue

Section

Student Abstract Track