| Summary: | Anomaly detection (AD) and anomaly segmentation (AS) is a large part of various
industrial setting such as the manufacturing industry. The improvement AD/AS
methods could potentially save companies cost and improve the overall workflow. In
recent years, AD/AS methodology with the use of computer vision (CV) has
drastically improved with some models reaching near perfect accuracy in the MVTec
dataset. However, most of these models have been trained on a large dataset which
may not be feasible for some industries. In this paper, we aim to breach the gap and
propose a training paradigm that will allow for preexisting AD/AS methodology to
be applied in such scenarios.
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