After a surge in popularity of supervised Deep Learning, the desire to reduce the dependence on curated, labelled data sets and to leverage the vast quantities of unlabelled data available recently triggered renewed interest in unsupervised learning algorithms. Despite a significantly improved performance due to approaches such as the identification of disentangled latent representations, contrastive learning, and clustering optimisations, the performance of unsupervised machine learning still falls short of its hypothesised potential. Machine learning has previously taken inspiration from neuroscience and cognitive science with great success. However, this has mostly been based on adult learners with access to labels and a vast amount of prior knowledge. In order 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. Conceptually, human infant learning is the closest biological parallel to artificial unsupervised learning, as infants too must learn useful representations from unlabelled data. In contrast to machine learning, these new representations are learned rapidly and from relatively few examples. Moreover, infants learn robust representations that can be used flexibly and efficiently in a number of different tasks and contexts. We identify five crucial factors enabling infants' quality and speed of learning, assess the extent to which these have already been exploited in machine learning, and propose how further adoption of these factors can give rise to previously unseen performance levels in unsupervised learning.

中文翻译：

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无监督机器学习的下一件大事：婴儿学习的五个教训

在监督式深度学习大受欢迎之后，减少对精选、标记数据集的依赖并利用最近可用的大量未标记数据的愿望引发了对无监督学习算法的新兴趣。尽管由于诸如识别解缠结的潜在表示，对比学习和聚类优化等方法而显着提高了性能，但无监督机器学习的性能仍然低于其假设的潜力。机器学习以前从神经科学和认知科学中获得灵感并取得了巨大成功。然而，这主要是基于可以访问标签和大量先验知识的成人学习者。为了推动无监督机器学习向前发展，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。从概念上讲，人类婴儿学习是与人工无监督学习最接近的生物学平行，因为婴儿也必须从未标记的数据中学习有用的表示。与机器学习相反，这些新的表示可以从相对较少的例子中快速学习。此外，婴儿学习稳健的表征，可以在许多不同的任务和环境中灵活有效地使用。我们确定了提高婴儿学习质量和速度的五个关键因素，评估这些因素在机器学习中已被利用的程度，