Its pretty common for an ML model to lose accuracy at various points during training. But presumably that means they are worse so why do we take the last version instead of the one that had the highest accuracy?
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Your goal is to find the highest mountain. but you’re kind of blind, and can only measure how tall the mountain is when you reach the peak of it. Once you reach that peak you might need to try a different mountain, which requires going back down.
There are also situations where the AI is recognizing specific images in the training data, rather than the characteristics of those images you want to distinguish. Or it might get hung up on a correlation in the training data, for example if you have a model that’s supposed to distinguish dogs from cats, you might accidentally make a leash detecting AI, and need to add a bunch of images of dogs without leashes to steer the AI back to what you want, even if it means you perform worse on the original data set.
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