  Datasets from single-molecule experiments often reflect a large variety of
molecular behaviour. The exploration of such datasets can be challenging,
especially if knowledge about the data is limited and a priori assumptions
about expected data characteristics are to be avoided. Indeed, searching for
pre-defined signal characteristics is sometimes useful, but it can also lead to
information loss and the introduction of expectation bias. Here, we demonstrate
how Transfer Learning-enhanced dimensionality reduction can be employed to
identify and quantify hidden features in single-molecule charge transport data,
in an unsupervised manner. Taking advantage of open-access neural networks
trained on millions of seemingly unrelated image data, our results also show
how Deep Learning methodologies can readily be employed, even if the amount of
problem-specific, 'own' data is limited.
