Class-Adaptive Sampling Policy for Efficient Continual Learning
Hossein Rezaei Mohammad Sabokrou
Continual learning (CL) aims to acquire new knowledge while preserving
information from previous experiences without forgetting. Though buffer-based
methods (i.e., retaining samples from previous tasks) have achieved acceptable
performance, determining how to allocate the buffer remains a critical
challenge. Most recent research focuses on refining these methods but often
fails to sufficiently consider the varying influence of samples on the learning
process, and frequently overlooks the complexity of the classes/concepts being
learned. Generally, these methods do not directly take into account the
contribution of individual classes. However, our investigation indicates that
more challenging classes necessitate preserving a larger number of samples
compared to less challenging ones. To address this issue, we propose a novel
method and policy named 'Class-Adaptive Sampling Policy' (CASP), which
dynamically allocates storage space within the buffer. By utilizing concepts of
class contribution and difficulty, CASP adaptively manages buffer space,
allowing certain classes to occupy a larger portion of the buffer while
reducing storage for others. This approach significantly improves the
efficiency of knowledge retention and utilization. CASP provides a versatile
solution to boost the performance and efficiency of CL. It meets the demand for
dynamic buffer allocation, accommodating the varying contributions of different
classes and their learning complexities over time.