Version 1
: Received: 2 November 2022 / Approved: 4 November 2022 / Online: 4 November 2022 (07:29:50 CET)
How to cite:
Schlagenhauf, T.; Scheurenbrand, T. Cross-domain Transfer of Defect Features in Technical Domains based on Partial Target Data. Preprints2022, 2022110090. https://doi.org/10.20944/preprints202211.0090.v1
Schlagenhauf, T.; Scheurenbrand, T. Cross-domain Transfer of Defect Features in Technical Domains based on Partial Target Data. Preprints 2022, 2022110090. https://doi.org/10.20944/preprints202211.0090.v1
Schlagenhauf, T.; Scheurenbrand, T. Cross-domain Transfer of Defect Features in Technical Domains based on Partial Target Data. Preprints2022, 2022110090. https://doi.org/10.20944/preprints202211.0090.v1
APA Style
Schlagenhauf, T., & Scheurenbrand, T. (2022). Cross-domain Transfer of Defect Features in Technical Domains based on Partial Target Data. Preprints. https://doi.org/10.20944/preprints202211.0090.v1
Chicago/Turabian Style
Schlagenhauf, T. and Tim Scheurenbrand. 2022 "Cross-domain Transfer of Defect Features in Technical Domains based on Partial Target Data" Preprints. https://doi.org/10.20944/preprints202211.0090.v1
Abstract
A common challenge in real-world classification scenarios with sequentially appending target domain data is insufficient training datasets during the training phase. Therefore, conventional deep learning and transfer learning classifiers are not applicable especially when individual classes are not represented or are severely underrepresented at the outset. Domain Generalization approaches reach their limits when domain shifts become too large, making them occasionally unsuitable as well. In many (technical) domains, however, it is only the defect/ worn/ reject classes that are insufficiently represented, while the non-defect class is often available from the beginning. The proposed classification approach addresses such conditions and is based on a CNN encoder. Following a contrastive learning approach, it is trained with a modified triplet loss function using two datasets: Besides the non-defective target domain class (= 1st dataset), a state-of-the-art labeled source domain dataset that contains highly related classes (e.g., a related manufacturing error or wear defect) but originates from a (highly) different domain (e.g., different product, material, or appearance) (= 2nd dataset) is utilized. The approach learns the classification features from the source domain dataset while at the same time learning the differences between the source and the target domain in a single training step, aiming to transfer the relevant features to the target domain. The classifier becomes sensitive to the classification features and – by architecture – robust against the highly domain-specific context. The approach is benchmarked in a technical and a non-technical domain and shows convincing classification results. In particular, it is shown that the domain generalization capabilities and classification results are improved by the proposed architecture, allowing for larger domain shifts between source and target domains.
Keywords
domain generalization; contrastive learning; classification; deep learning; encoder; Zero-Shot Learning
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
