Many contrastive and meta-learning approaches learn representations by identifying common features in multiple views. However, the formalism for these approaches generally assumes features to be shared across views to be captured coherently. We consider the problem of learning a unified representation from partial observations, where useful features may be present in only some of the views. We approach this through a probabilistic formalism enabling views to map to representations with different levels of uncertainty in different components; these views can then be integrated with one another through marginalisation over that uncertainty. Our approach, Partial Observation Experts Modelling (POEM), then enables us to meta-learn consistent representations from partial observations. We evaluate our approach on an adaptation of a comprehensive few-shot learning benchmark, Meta-Dataset, and demonstrate the benefits of POEM over other meta-learning methods at representation learning from partial observations. We further demonstrate the utility of POEM by meta-learning to represent an environment from partial views observed by an agent exploring the environment.

翻译：许多对比学习和元学习方法通过识别多视图中的共同特征来学习表示。然而，这些方法的形式化假设通常要求在不同视图间一致地捕获共享特征。我们考虑了从部分观测中学习统一表示的问题，其中有用的特征可能仅存在于某些视图中。我们通过一种概率形式化方法来解决这一问题，该方法允许视图映射到具有不同分量不确定性水平的表示；这些视图随后可通过边缘化该不确定性来相互整合。我们的方法——部分观测专家建模（POEM）——使我们能够从部分观测中元学习一致的表示。我们在适应性修改的综合性小样本学习基准Meta-Dataset上评估了该方法，并展示了POEM在从部分观测中学习表示方面优于其他元学习方法的优势。我们进一步通过元学习表示一个由探索环境的智能体观测到的部分视图中的环境，来证明POEM的实用性。