Continual learning and few-shot learning are important frontiers in progress towards broader Machine Learning (ML) capabilities. There is a growing body of work in both, but few works combining the two. One exception is the Continual few-shot Learning (CFSL) framework of Antoniou et al. arXiv:2004.11967. 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 modify CFSL to make it more comparable to standard continual learning experiments, where usually a much larger number of classes are presented. Second, we introduce 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 of ML model performance under these conditions, we selected representative baseline models from the original CFSL work and added a model variant with replay. As expected, learning more classes is more difficult than the original CFSL experiments, and interestingly, the way in which image instances and classes are presented affects classification performance. Surprisingly, accuracy in the baseline instance test is comparable to other classification tasks, but poor given significant occlusion and noise. The use of replay for consolidation improves performance substantially for both types of tasks, but particularly the instance test.

翻译：持续学习和少样本学习是推动机器学习更广泛能力发展的重要前沿领域。尽管这两个领域的研究日益增多，但将两者结合的工作却很少。Antoniou等人的持续少样本学习框架（arXiv:2004.11967）是少数例外之一。在本研究中，我们从两个方向扩展了持续少样本学习框架，旨在捕捉智能体在真实世界条件下更广泛的挑战。首先，我们修改框架使其与标准持续学习实验更具可比性（通常涉及大量类别）。其次，我们引入“实例测试”，要求识别类别的特定实例——这一动物认知能力通常在机器学习中被忽视。为了初步探索这些条件下的机器学习模型性能，我们从原始持续少样本学习工作中选取了代表性基线模型，并增加了一种带重放的模型变体。正如预期，学习更多类别的难度高于原始持续少样本学习实验，且有趣的是，图像实例与类别的呈现方式会影响分类性能。令人惊讶的是，基线实例测试的准确率与其他分类任务相当，但在显著遮挡和噪声条件下表现较差。使用重放进行巩固能显著提升两类任务的性能，尤其是实例测试。