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Add SSIM as Loss Function #97788

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bluehyena opened this issue Mar 28, 2023 · 1 comment
Open

Add SSIM as Loss Function #97788

bluehyena opened this issue Mar 28, 2023 · 1 comment
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feature A request for a proper, new feature. module: loss Problem is related to loss function needs research We need to decide whether or not this merits inclusion, based on research world triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module

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@bluehyena
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馃殌 The feature, motivation and pitch

Hello, I am doing research in the field of Unsupervised Anomaly Detection. In Unsupervised Anomaly Detection, you can segment an anomaly area through Reconstruction Error, but in the case of areas with ambiguous boundaries, better performance is shown when using loss using SSIM than using existing losses. Therefore, I would like to be able to use SSIM as the loss function of pytorch. Thank you :)

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@janeyx99 janeyx99 added feature A request for a proper, new feature. module: loss Problem is related to loss function triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module needs research We need to decide whether or not this merits inclusion, based on research world labels Mar 28, 2023
@janeyx99
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Thanks for raising this up! PyTorch Core has some pretty strict rules when it comes to adding a new Optimizer or Module function or feature: https://github.com/pytorch/pytorch/wiki/Developer-FAQ#i-have-a-new-function-or-feature-id-like-to-add-to-pytorch-should-it-be-in-pytorch-core-or-a-library-like-torchvision.

Currently, SSIM seems best suited to be implemented in a separate library (or using an existing one), and for the future, we'll happily reconsider if this function increases in popularity. We can leave this issue open to gauge popularity among other researchers and users.

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Labels
feature A request for a proper, new feature. module: loss Problem is related to loss function needs research We need to decide whether or not this merits inclusion, based on research world triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module
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