γILP is a differentiable pipeline for inducing first-order rules from unlabeled image data, showing strong performance on symbolic relational datasets, relational images, and pure image datasets such as Kandinsky patterns.
Deep residual learning for image recognition
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Visual Perceptual to Conceptual First-Order Rule Learning Networks
γILP is a differentiable pipeline for inducing first-order rules from unlabeled image data, showing strong performance on symbolic relational datasets, relational images, and pure image datasets such as Kandinsky patterns.