Sample-efficient active learning for materials informatics using integrated posterior variance

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Instead of perturbing each pixel in the input image at random, we can choose to dither by a predetermined amount depending on the pixel’s position in the image. This can be achieved using a threshold map; a small, fixed-size matrix where each entry tells us the amount by which to perturb the input value , producing the dithered value . This matrix is tiled across the input image and sampled for every pixel during the dithering process.  The following describes a dithering function for a 4×4 matrix given the pixel raster coordinates :

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