  Neural networks have greatly boosted performance in computer vision by
learning powerful representations of input data. The drawback of end-to-end
training for maximal overall performance are black-box models whose hidden
representations are lacking interpretability: Since distributed coding is
optimal for latent layers to improve their robustness, attributing meaning to
parts of a hidden feature vector or to individual neurons is hindered. We
formulate interpretation as a translation of hidden representations onto
semantic concepts that are comprehensible to the user. The mapping between both
domains has to be bijective so that semantic modifications in the target domain
correctly alter the original representation. The proposed invertible
interpretation network can be transparently applied on top of existing
architectures with no need to modify or retrain them. Consequently, we
translate an original representation to an equivalent yet interpretable one and
backwards without affecting the expressiveness and performance of the original.
The invertible interpretation network disentangles the hidden representation
into separate, semantically meaningful concepts. Moreover, we present an
efficient approach to define semantic concepts by only sketching two images and
also an unsupervised strategy. Experimental evaluation demonstrates the wide
applicability to interpretation of existing classification and image generation
networks as well as to semantically guided image manipulation.
