We could use the same approach taken by many research works that generate images and paint the square with a stack of deconvolution (transposed convolution) layers. To test this idea, we created a dataset consisting of randomly placed 9 9 squares on a 64 64 canvas, as shown in Figure 1b. To evaluate how well models generalize, we define two train/test splits: a uniform split, where all possible center locations are randomly divided 80 percent/20 percent into train vs. test sets, and a quadrant split, where the canvas is divided into four quadrants: squares centered in the first three quadrants are put in the train set and squares in the last quadrant in the test set.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)