Active Learning for Deep Neural Networks on Edge Devices

Understand

When dealing with deep neural network (DNN) applications on edge devices, continuously updating the model is important.

• Although updating a model with real incoming data is ideal, using all of them is not always feasible due to limits, such as labeling and communication costs.
• Thus, it is necessary to filter and select the data to use for training (i.e., active learning) on the device.
• In this paper, we formalize a practical active learning problem for DNNs on edge devices and propose a general task-agnostic framework to tackle this problem, which reduces it to a stream submodular maximization.