Title: Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks,
Abstract: In an effort to understand the meaning of the intermediate representations
captured by deep networks, recent papers have tried to associate specific
semantic concepts to individual neural network filter responses, where
interesting correlations are often found, largely by focusing on extremal
filter responses. In this paper, we show that this approach can favor
easy-to-interpret cases that are not necessarily representative of the average
behavior of a representation.
A more realistic but harder-to-study hypothesis is that semantic
representations are distributed, and thus filters must be studied in
conjunction. In order to investigate this idea while enabling systematic
visualization and quantification of multiple filter responses, we introduce the
Net2Vec framework, in which semantic concepts are mapped to vectorial
embeddings based on corresponding filter responses. By studying such
embeddings, we are able to show that 1., in most cases, multiple filters are
required to code for a concept, that 2., often filters are not concept specific
and help encode multiple concepts, and that 3., compared to single filter
activations, filter embeddings are able to better characterize the meaning of a
representation and its relationship to other concepts.