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We explore neural painters, a generative model for brushstrokes learned froma real non-differentiable and non-deterministic painting program. We show thatwhen training an agent to "paint" images using brushstrokes, using adifferentiable neural painter leads to much faster convergence. We propose amethod for encouraging this agent to follow human-like strokes whenreconstructing digits. We also explore the use of a neural painter as adifferentiable image parameterization. By directly optimizing brushstrokes toactivate neurons in a pre-trained convolutional network, we can directlyvisualize ImageNet categories and generate "ideal" paintings of each class.Finally, we present a new concept called intrinsic style transfer. Byminimizing only the content loss from neural style transfer, we allow theartistic medium, in this case, brushstrokes, to naturally dictate the resultingstyle.

Neural Painters: A learned differentiable constraint for generating brushstroke paintings