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It has long been speculated that deep neural networks function by discoveringa hierarchical set of domain-specific core concepts or patterns, which arefurther combined to recognize even more elaborate concepts for theclassification or other machine learning tasks. Meanwhile disentangling theactual core concepts engrained in the word embeddings (like word2vec or BERT)or deep convolutional image recognition neural networks (like PG-GAN) isdifficult and some success there has been achieved only recently. In this paperwe propose a novel neural network nonlinearity named DifferentiableDisentanglement Filter (DDF) which can be transparently inserted into anyexisting neural network layer to automatically disentangle the core conceptsused by that layer. The DDF probe is inspired by the obscure properties of thehyper-dimensional computing theory. The DDF proof-of-concept implementation isshown to disentangle concepts within the neural 3D scene representation - atask vital for visual grounding of natural language narratives.

Differentiable Disentanglement Filter: an Application Agnostic Core Concept Discovery Probe