The figure is showing how the matrix is added to the neural network and the benefit it provides in separating classes.
The figure is showing how the matrix is added to the neural network and the benefit it provides in separating classes. When current machine learning systems are trained on a dataset of images, they struggle to learn from examples that don't occur often. A UvA PhD candidate now came up with an elegant and easy to implement solution to this problem. Imagine a large number of medical scans from which doctors want to know which ones show a tumor and which ones don't. Quite probably the training data contain many more scans without a tumor than with a tumor. When an automatic image classification system is trained on such a biased dataset, and no extra measures are taken, the chances are high that it under diagnoses tumors, which of course is undesirable. Generally speaking, the less a particular example appears in a dataset, the more it is ignored by current day machine learning systems.
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