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Constrained image generation using binarized neural networks with decision procedures. (English) Zbl 1511.68297

Beyersdorff, Olaf (ed.) et al., Theory and applications of satisfiability testing – SAT 2018. 21st international conference, SAT 2018, held as part of the Federated Logic Conference, FloC 2018, Oxford, UK, July 9–12, 2018. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 10929, 438-449 (2018).
Summary: We consider the problem of binary image generation with given properties. This problem arises in a number of practical applications, including generation of artificial porous medium for an electrode of lithium-ion batteries, for composed materials, etc. A generated image represents a porous medium and, as such, it is subject to two sets of constraints: topological constraints on the structure and process constraints on the physical process over this structure. To perform image generation we need to define a mapping from a porous medium to its physical process parameters. For a given geometry of a porous medium, this mapping can be done by solving a partial differential equation (PDE). However, embedding a PDE solver into the search procedure is computationally expensive. We use a binarized neural network to approximate a PDE solver. This allows us to encode the entire problem as a logical formula. Our main contribution is that, for the first time, we show that this problem can be tackled using decision procedures. Our experiments show that our model is able to produce random constrained images that satisfy both topological and process constraints.
For the entire collection see [Zbl 1390.68015].

MSC:

68U10 Computing methodologies for image processing
68T05 Learning and adaptive systems in artificial intelligence
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
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