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AdaXpert: Adapting Neural Architecture for Growing Data
Shuaicheng Niu · Jiaxiang Wu · Guanghui Xu · Yifan Zhang · Yong Guo · Peilin Zhao · Peng Wang · Mingkui Tan

Wed Jul 21 07:25 PM -- 07:30 PM (PDT) @ None

In real-world applications, data often come in a growing manner, where the data volume and the number of classes may increase dynamically. This will bring a critical challenge for learning: given the increasing data volume or the number of classes, one has to instantaneously adjust the neural model capacity to obtain promising performance. Existing methods either ignore the growing nature of data or seek to independently search an optimal architecture for a given dataset, and thus are incapable of promptly adjusting the architectures for the changed data. To address this, we present a neural architecture adaptation method, namely Adaptation eXpert (AdaXpert), to efficiently adjust previous architectures on the growing data. Specifically, we introduce an architecture adjuster to generate a suitable architecture for each data snapshot, based on the previous architecture and the different extent between current and previous data distributions. Furthermore, we propose an adaptation condition to determine the necessity of adjustment, thereby avoiding unnecessary and time-consuming adjustments. Extensive experiments on two growth scenarios (increasing data volume and number of classes) demonstrate the effectiveness of the proposed method.

Author Information

Shuaicheng Niu (South China University of Technology)
Jiaxiang Wu (Tencent AI Lab)
Guanghui Xu (South China University of Technology)
Yifan Zhang (National University of Singapore)
Yong Guo (South China University of Technology)
Peilin Zhao (Tencent AI Lab)
Peng Wang (Northwestern Polytechnical University)
Mingkui Tan (South China University of Technology)

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