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Apart from discriminative models for classification and object detectiontasks, the application of deep convolutional neural networks to basic researchutilizing natural imaging data has been somewhat limited; particularly in caseswhere a set of interpretable features for downstream analysis is needed, a keyrequirement for many scientific investigations. We present an algorithm andtraining paradigm designed specifically to address this: decontextualizedhierarchical representation learning (DHRL). By combining a generative modelchaining procedure with a ladder network architecture and latent spaceregularization for inference, DHRL address the limitations of small datasetsand encourages a disentangled set of hierarchically organized features. Inaddition to providing a tractable path for analyzing complex hierarchalpatterns using variation inference, this approach is generative and can bedirectly combined with empirical and theoretical approaches. To highlight theextensibility and usefulness of DHRL, we demonstrate this method in applicationto a question from evolutionary biology.

Decontextualized learning for interpretable hierarchical representations of visual patterns