The binding problem in human cognition, concerning how the brain represents and connects objects within a fixed network of neural connections, remains a subject of intense debate. Most machine learning efforts addressing this issue in an unsupervised setting have focused on slot-based methods, which may be limiting due to their discrete nature and difficulty to express uncertainty. Recently, the Complex AutoEncoder was proposed as an alternative that learns continuous and distributed object-centric representations. However, it is only applicable to simple toy data. In this paper, we present Rotating Features, a generalization of complex-valued features to higher dimensions, and a new evaluation procedure for extracting objects from distributed representations. Additionally, we show the applicability of our approach to pre-trained features. Together, these advancements enable us to scale distributed object-centric representations from simple toy to real-world data. We believe this work advances a new paradigm for addressing the binding problem in machine learning and has the potential to inspire further innovation in the field.

翻译：人类认知中的绑定问题——即大脑如何在固定的神经连接网络中表征和连接物体——仍是激烈争论的议题。大多数无监督环境下解决该问题的机器学习方法都聚焦于基于槽的方法，这类方法因其离散性以及难以表达不确定性而存在局限性。近期，复合自编码器被提出作为替代方案，能够学习连续且分布式的以物体为中心的表征。然而，该方法仅适用于简单的玩具数据。本文提出了旋转特征（即复值特征向高维的推广）以及一种从分布式表征中提取物体的新评估流程。此外，我们展示了该方法在预训练特征上的适用性。这些进展共同使我们能够将分布式以物体为中心的表征从简单玩具数据扩展到真实世界数据。我们相信这项工作推动了机器学习中解决绑定问题的新范式，并有望激发该领域的进一步创新。