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Task-Adaptive Incremental Learning for Intelligent Edge Devices
Convolutional Neural Networks (CNNs) are used for a wide range of
image-related tasks such as image classification and object detection. However,
a large pre-trained CNN model contains a lot of redundancy considering the
task-specific edge applications. Also, the statically pre-trained model could
not efficiently handle the dynamic data in the real-world application. The CNN
training data and their labels are collected in an incremental manner. To
tackle the above two challenges, we proposed TeAM a task-adaptive incremental
learning framework for CNNs in intelligent edge devices. Given a pre-trained
large model, TeAM can configure it into any specialized model for dedicated
edge applications. The specialized model can be quickly fine-tuned with local
data to achieve very high accuracy. Also, with our global aggregation and
incremental learning scheme, the specialized CNN models can be collaboratively
aggregated to an enhanced global model with new training data.Comment: 2 page