  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.
