However, convolutional neural nets trained on images learn to detect simple feature such as edges, corners, color blobs, etc., in their early layers. These features are useful for all kinds of image processing tasks. Therefore, we can reuse these nets already trained on a large dataset of images such as ImageNet, and fine-tune them to build a classifier for a new problem using only a few labeled elements.