Deep learning requires a lot of labeled data. The more thorough and accurate the labeling, the better the performance of the network.
Labeling defines those features in the data that you want the network to recognize and classify. But data labeling is extremely time-consuming and error-prone—imagine manually drawing bounding boxes around thousands of images or having to define the classes of every single pixel in those images.
With MATLAB you can automate the more time-consuming parts of this process by using interactive tools. For example:
Image labeler and video labeler apps classify regions of an image and automatically apply that classification through every frame of a video.
Signal and audio labeler apps, like the image labeler, have built-in automation capabilities—in this case, to speed up labeling of signal data.
Big-image labeler app lets you label large images interactively. You don’t have to worry about extracting patches, labeling each patch, and reconstructing the image. Instead, you can move around the image and label different parts of it.