Expanding continual few-shot learning benchmarks to include recognition of specific instances.

ABSTRACT: Continual learning and few-shot learning are important frontiers in progress toward broader Machine Learning (ML) capabilities. Recently, there has been intense interest in combining both. One of the first examples to do so was the Continual few-shot Learning (CFSL) framework of Antoniou et al. (2020). In this study, we extend CFSL in two ways that capture a broader range of challenges, important for intelligent agent behaviour in real-world conditions. First, we increased the number of classes by an order of magnitude, making the results more comparable to standard continual learning experiments. Second, we introduced an 'instance test' which requires recognition of specific instances of classes-a capability of animal cognition that is usually neglected in ML. For an initial exploration o

SUBMITTER: Kowadlo G 

PROVIDER: S-EPMC11226023 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature