# Task-Adaptive Incremental Learning for Intelligent Edge Devices Source: [Task-Adaptive Incremental Learning for Intelligent Edge Devices]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:59:47.071Z Authors: Zhuwei Qin, Fuxun Yu, Xiang Chen Published: 2019-10-07T22:57:31.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()