Neural Architecture Search for Class-incremental Learning

Understand

In class-incremental learning, a model learns continuously from a sequential data stream in which new classes occur.

• Existing methods often rely on static architectures that are manually crafted.
• These methods can be prone to capacity saturation because a neural network's ability to generalize to new concepts is limited by its fixed capacity.
• To understand how to expand a continual learner, we focus on the neural architecture design problem in the context of class-incremental learning: at each time step, the learner must optimize its performance on all classes observed so far by selecting the most competitive neural architecture.