%0 Journal Article %1 ghojogh2020theoretical %A Ghojogh, Benyamin %A Karray, Fakhri %A Crowley, Mark %D 2020 %K generative-models index structure %T Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders %U http://arxiv.org/abs/2004.01864 %X Generative models and inferential autoencoders mostly make use of $\ell_2$ norm in their optimization objectives. In order to generate perceptually better images, this short paper theoretically discusses how to use Structural Similarity Index (SSIM) in generative models and inferential autoencoders. We first review SSIM, SSIM distance metrics, and SSIM kernel. We show that the SSIM kernel is a universal kernel and thus can be used in unconditional and conditional generated moment matching networks. Then, we explain how to use SSIM distance in variational and adversarial autoencoders and unconditional and conditional Generative Adversarial Networks (GANs). Finally, we propose to use SSIM distance rather than $\ell_2$ norm in least squares GAN.

@article{ghojogh2020theoretical, abstract = {Generative models and inferential autoencoders mostly make use of $\ell_2$ norm in their optimization objectives. In order to generate perceptually better images, this short paper theoretically discusses how to use Structural Similarity Index (SSIM) in generative models and inferential autoencoders. We first review SSIM, SSIM distance metrics, and SSIM kernel. We show that the SSIM kernel is a universal kernel and thus can be used in unconditional and conditional generated moment matching networks. Then, we explain how to use SSIM distance in variational and adversarial autoencoders and unconditional and conditional Generative Adversarial Networks (GANs). Finally, we propose to use SSIM distance rather than $\ell_2$ norm in least squares GAN.}, added-at = {2020-04-07T12:43:06.000+0200}, author = {Ghojogh, Benyamin and Karray, Fakhri and Crowley, Mark}, biburl = {https://www.bibsonomy.org/bibtex/24aec5acd30291bd3262aafbda4fc84bc/kirk86}, description = {[2004.01864] Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders}, interhash = {6f90f99dc7f29958f48210440561f709}, intrahash = {4aec5acd30291bd3262aafbda4fc84bc}, keywords = {generative-models index structure}, note = {cite arxiv:2004.01864Comment: Accepted (to appear) in International Conference on Image Analysis and Recognition (ICIAR) 2020, Springer}, timestamp = {2020-04-07T12:43:06.000+0200}, title = {Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders}, url = {http://arxiv.org/abs/2004.01864}, year = 2020 }