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.