  Apart from discriminative models for classification and object detection
tasks, the application of deep convolutional neural networks to basic research
utilizing natural imaging data has been somewhat limited; particularly in cases
where a set of interpretable features for downstream analysis is needed, a key
requirement for many scientific investigations. We present an algorithm and
training paradigm designed specifically to address this: decontextualized
hierarchical representation learning (DHRL). By combining a generative model
chaining procedure with a ladder network architecture and latent space
regularization for inference, DHRL address the limitations of small datasets
and encourages a disentangled set of hierarchically organized features. In
addition to providing a tractable path for analyzing complex hierarchal
patterns using variation inference, this approach is generative and can be
directly combined with empirical and theoretical approaches. To highlight the
extensibility and usefulness of DHRL, we demonstrate this method in application
to a question from evolutionary biology.
