  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. In many technical
domains, however, it is only the defect or 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.
