Abstract
Current state-of-the-art object-centric models use slots and attention-based routing for binding. However, this class of models has several conceptual limitations: the number of slots is hardwired; all slots have equal capacity; training has high computational cost; there are no object-level relational factors within slots. Synchrony-based models in principle can address these limitations by using complex-valued activations which store binding information in their phase components. However, working examples of such synchrony-based models have been developed only very recently, and are still limited to toy grayscale datasets and simultaneous storage of less than three objects in practice. Here we introduce architectural modifications and a novel contrastive learning method that greatly improve the state-of-the-art synchrony-based model. For the first time, we obtain a class of synchrony-based models capable of discovering objects in an unsupervised manner in multi-object color datasets and simultaneously representing more than three objects
Abstract (translated)
当前先进的对象中心模型使用孔和注意力based routing进行绑定。然而，这种一类模型有几个概念上的限制：孔的数量是硬编的；所有孔都具有相等的能力；训练有高计算成本；在孔中不存在对象级别的关系因素。同步模型理论上可以通过使用复杂的值激活来解决这些问题，并将绑定信息存储在相位组件中。然而，最近才开发了这种同步模型的工作例子，仍然局限于玩具灰度数据集和实践中所存储的小于3个对象。在这里我们介绍了建筑修改和创新的对抗学习方法，极大地改进了先进的同步模型。首次，我们获得了一类能够在多对象彩色数据集中 unsupervised 地发现对象并同时代表多于3个对象的同步模型。
URL
https://arxiv.org/abs/2305.15001
