  Interpretability is an important property for visual models as it helps
researchers and users understand the internal mechanism of a complex model.
However, generating semantic explanations about the learned representation is
challenging without direct supervision to produce such explanations. We propose
a general framework, Latent Visual Semantic Explainer (LaViSE), to teach any
existing convolutional neural network to generate text descriptions about its
own latent representations at the filter level. Our method constructs a mapping
between the visual and semantic spaces using generic image datasets, using
images and category names. It then transfers the mapping to the target domain
which does not have semantic labels. The proposed framework employs a modular
structure and enables to analyze any trained network whether or not its
original training data is available. We show that our method can generate novel
descriptions for learned filters beyond the set of categories defined in the
training dataset and perform an extensive evaluation on multiple datasets. We
also demonstrate a novel application of our method for unsupervised dataset
bias analysis which allows us to automatically discover hidden biases in
datasets or compare different subsets without using additional labels. The
dataset and code are made public to facilitate further research.
