Decision boundaries and convex hulls in the feature space that deep learning functions learn from images
Decision boundaries and convex hulls in the feature space that deep learning functions learn from images
Abstract The success of deep neural networks in image classification and learning can be partly attributed to the features they extract from images. It is often speculated about the properties of a low-dimensional manifold that models extract and learn from images. However, there is not sufficient understanding about this low-dimensional …