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I've been looking at autoencoders for disparate uses such as dimension reduction, blurring or sharpening images and data denoising.

What methods are used to determine acceptable loss levels for autoencoders?

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Acceptable loss levels depend on the specific domain, dataset, and model.

Loss levels can be compared within model (e.g., training loss by epoch) or across models (e.g., autoencoders with different levels of compression).

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