  In this study, we show that diffusion models can be used in industrial
scenarios to improve the data augmentation procedure in the context of surface
defect detection. In general, defect detection classifiers are trained on
ground-truth data formed by normal samples (negative data) and samples with
defects (positive data), where the latter are consistently fewer than normal
samples. For these reasons, state-of-the-art data augmentation procedures add
synthetic defect data by superimposing artifacts to normal samples. This leads
to out-of-distribution augmented data so that the classification system learns
what is not a normal sample but does not know what a defect really is. We show
that diffusion models overcome this situation, providing more realistic
in-distribution defects so that the model can learn the defect's genuine
appearance. We propose a novel approach for data augmentation that mixes
out-of-distribution with in-distribution samples, which we call In&Out. The
approach can deal with two data augmentation setups: i) when no defects are
available (zero-shot data augmentation) and ii) when defects are available,
which can be in a small number (few-shot) or a large one (full-shot). We focus
the experimental part on the most challenging benchmark in the
state-of-the-art, i.e., the Kolektor Surface-Defect Dataset 2, defining the new
state-of-the-art classification AP score under weak supervision of .782. The
code is available at https://github.com/intelligolabs/in_and_out.
