Learning to Edit Visual Programs with Self-Supervision

Neural Information Processing Systems 

We design a system that learns how to edit visual programs. Our edit network consumes a complete input program and a visual target. From this input, we task our network with predicting a local edit operation that could be applied to the input program to improve its similarity to the target.

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