Maximum entropy methods for texture synthesis: theory and practice
This addresses texture synthesis and style transfer problems in computer vision, offering a theoretical foundation with error bounds, but it appears incremental as it builds on existing maximum entropy and sampling methods.
The paper tackles texture synthesis by proposing a maximum entropy framework with an alternate sampling/minimization scheme, showing theoretically that its error can be explicitly bounded with polynomial dimension dependence and experimentally comparing it to state-of-the-art methods.
Recent years have seen the rise of convolutional neural network techniques in exemplar-based image synthesis. These methods often rely on the minimization of some variational formulation on the image space for which the minimizers are assumed to be the solutions of the synthesis problem. In this paper we investigate, both theoretically and experimentally, another framework to deal with this problem using an alternate sampling/minimization scheme. First, we use results from information geometry to assess that our method yields a probability measure which has maximum entropy under some constraints in expectation. Then, we turn to the analysis of our method and we show, using recent results from the Markov chain literature, that its error can be explicitly bounded with constants which depend polynomially in the dimension even in the non-convex setting. This includes the case where the constraints are defined via a differentiable neural network. Finally, we present an extensive experimental study of the model, including a comparison with state-of-the-art methods and an extension to style transfer.