WealthAdapt: A General Network Adaptation Framework for Small Data Tasks

Bingyan Liu, Yao Guo, Xiangqun Chen

Abstract: In this paper, we propose a general network adaptation framework, namely WealthAdapt, to effectively adapt a large network for small data tasks, with the assistance of a wealth of related data. While many existing algorithms have proposed network adaptation techniques for resource-constrained systems, they typically implement network adaptation based on a large dataset and do not perform well when facing small data tasks. Because small data have poor feature expression ability, it may result in incorrect filter selection and overfitting during fine-tuning in the network adaptation process. In WealthAdapt, we first expand the target small data task with the wealth of big data, before we perform network adaptation, in order to enrich the features and improve the fine-tuning performance during adaptation. We formally establish network adaptation for small data tasks as an optimization problem and solve it through two main techniques:model-based fast selection and wealth-incorporated iteration adaptation. Experimental results demonstrate that our framework is applicable to both the vanilla convolutional network VGG-16 and more complex modern architecture ResNet-50, outperforming several state-of-the-art network adaptation pipelines on multiple visual classification tasks including general object recognition, fine-grained object recognition and scene recognition.