The desire to reduce the dependence on curated, labeled datasets and to leverage the vast quantities of unlabeled data has triggered renewed interest in unsupervised (or self-supervised) learning algorithms. Despite improved performance due to approaches such as the identification of disentangled latent representations, contrastive learning and clustering optimizations, unsupervised machine learning still falls short of its hypothesized potential as a breakthrough paradigm enabling generally intelligent systems. Inspiration from cognitive (neuro)science has been based mostly on adult learners with access to labels and a vast amount of prior knowledge. To push unsupervised machine learning forward, we argue that developmental science of infant cognition might hold the key to unlocking the next generation of unsupervised learning approaches. We identify three crucial factors enabling infants’ quality and speed of learning: (1) babies’ information processing is guided and constrained; (2) babies are learning from diverse, multimodal inputs; and (3) babies’ input is shaped by development and active learning. We assess the extent to which these insights from infant learning have already been exploited in machine learning, examine how closely these implementations resemble the core insights, and propose how further adoption of these factors can give rise to previously unseen performance levels in unsupervised learning.

减少对经过整理的标记数据集的依赖并利用大量未标记数据的愿望引发了对无监督（或自我监督）学习算法的新兴趣。尽管由于诸如识别解开的潜在表示、对比学习和聚类优化等方法而提高了性能，但无监督机器学习仍然没有达到其作为实现普遍智能系统的突破性范式的假设潜力。认知（神经）科学的灵感主要基于能够接触标签和大量先验知识的成年学习者。为了推动无监督机器学习的发展，我们认为婴儿认知的发展科学可能是开启下一代无监督学习方法的关键。我们确定了影响婴儿学习质量和速度的三个关键因素：（1）婴儿的信息处理受到引导和约束；(2) 婴儿正在从多样化、多模式的输入中学习；(3) 婴儿的输入是由发展和主动学习形成的。我们评估了这些来自婴儿学习的见解在机器学习中的应用程度，检查了这些实现与核心见解的相似程度，并提出了进一步采用这些因素如何在无监督学习中提高以前看不见的性能水平。