How to Train Neural Networks With Fewer Data Using Active Learning

Author(s): Leon Eversberg

Originally published on Towards AI.

A state-of-the-art guide to the theory and practice of deep active learning

Oracle of Delphi. Photo by Walkerssk from Pixabay

One of the biggest problems in supervised deep learning is the scarcity of labeled training data. If you want to train your own deep learning model in the real world, you typically need to label a lot of data. However, the process of labeling data is usually tedious and costly. This is where active learning comes in.

Not all training data samples are equally valuable to the training process, by selecting only the most valuable training samples, active learning attempts to minimize the amount of labeled training data required.

Figure 1 illustrates the motivation behind… Read the full blog for free on Medium.

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Published via Towards AI