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Convolutional Neural Networks (CNNs) are used for a wide range ofimage-related tasks such as image classification and object detection. However,a large pre-trained CNN model contains a lot of redundancy considering thetask-specific edge applications. Also, the statically pre-trained model couldnot efficiently handle the dynamic data in the real-world application. The CNNtraining data and their labels are collected in an incremental manner. Totackle the above two challenges, we proposed TeAM a task-adaptive incrementallearning framework for CNNs in intelligent edge devices. Given a pre-trainedlarge model, TeAM can configure it into any specialized model for dedicatededge applications. The specialized model can be quickly fine-tuned with localdata to achieve very high accuracy. Also, with our global aggregation andincremental learning scheme, the specialized CNN models can be collaborativelyaggregated to an enhanced global model with new training data.

Task-Adaptive Incremental Learning for Intelligent Edge Devices