Transfer learning methods and models facilitating defect detection

Abstract

Methods and systems for training a model for automated defect detection of a product during manufacturing are provided herein. Such methods utilize a combination of supervised transfer learning through auxiliary tasks and a combination of supervised and unsupervised learning. The methods can utilize supervised transfer learning with expert labels on a generalized auxiliary task, such as product classification, which is transferred to more specific auxiliary tasks, such as identification of specific product features and/or anomaly detection, where additional expert labels are then applied to the anomalies, and another iteration of supervised learning further improves the model. The anomalies can correspond to features associated with defects, which can be induced experimentally to improve efficiency of the training procedure. The product can be a sample cartridge such that the model allows detection of faulty cartridges based on sample cartridge and/or manufacturing process data.