The Level Weighted Structural Similarity Loss: A Step Away from the MSE
This addresses the problem of poor spatial modeling in image reconstruction for researchers and practitioners using Auto-Encoders, though it is incremental as it builds on the SSIM metric.
The paper tackled the limitation of Mean Square Error (MSE) in image reconstruction by proposing a Level Weighted Structural Similarity (LWSSIM) loss for convolutional Auto-Encoders, which outperformed MSE and standard SSIM loss on common datasets.
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.