How saccadic vision might help with theinterpretability of deep networks

Abstract--We describe how some problems (interpretability, lack of object-orientedness) of modern deep networks potentially could be solved by adapting a biologically plausible saccadic mechanism of perception. A sketch of such a saccadic vision model is proposed. Proof of concept experimental results are provided to support the proposed approach. One of the most human-readable representations of a visual Deep convolutional networks are often used today in applied scene is the semantic scene graph: if it is present, the task problems as one of the basic components of learning systems. of generating the text describing the scene is trivial [7]. The On some tasks, for example, the task of modeling faces, it is nodes of such a graph are usually nouns that name objects possible to achieve representations with good interpretability on the stage. The node can be assigned its coordinates on the [2].