  Transfer learning schemes based on deep networks which have been trained on
huge image corpora offer state-of-the-art technologies in computer vision.
Here, supervised and semi-supervised approaches constitute efficient
technologies which work well with comparably small data sets. Yet, such
applications are currently restricted to application domains where suitable
deepnetwork models are readily available. In this contribution, we address an
important application area in the domain of biotechnology, the automatic
analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation,
where data characteristics are very dissimilar to existing domains and trained
deep networks cannot easily be adapted by classical transfer learning. We
propose a novel transfer learning scheme which expands a recently introduced
Twin-VAE architecture, which is trained on realistic and synthetic data, and we
modify its specialized training procedure to the transfer learning domain. In
the specific domain, often only few to no labels exist and annotations are
costly. We investigate a novel transfer learning strategy, which incorporates a
simultaneous retraining on natural and synthetic data using an invariant shared
representation as well as suitable target variables, while it learns to handle
unseen data from a different microscopy tech nology. We show the superiority of
the variation of our Twin-VAE architecture over the state-of-the-art transfer
learning methodology in image processing as well as classical image processing
technologies, which persists, even with strongly shortened training times and
leads to satisfactory results in this domain. The source code is available at
https://github.com/dstallmann/transfer_learning_twinvae, works cross-platform,
is open-source and free (MIT licensed) software. We make the data sets
available at https://pub.uni-bielefeld.de/record/2960030.
