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> meaning 20% accuracy is equal to 80% accuracy,

Only if your model is outputting a yes/no answer right? And that your definition of accuracy is "class with highest probability" (and not "N classes with highest prob")

If your dataset has more than 2 classes like MNIST, a super low accuracy only tells you to ignore the class the model guesses. It doesn't tell you which of the remaining classes is correct



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