Current state-of-the-art synchrony-based models encode object bindings with complex-valued activations and compute with real-valued weights in feedforward architectures. We argue for the computational advantages of a recurrent architecture with complex-valued weights. We propose a fully convolutional autoencoder, SynCx, that performs iterative constraint satisfaction: at each iteration, a hidden layer bottleneck encodes statistically regular configurations of features in particular phase relationships; over iterations, local constraints propagate and the model converges to a globally consistent configuration of phase assignments. Binding is achieved simply by the matrix-vector product operation between complex-valued weights and activations, without the need for additional mechanisms that have been incorporated into current synchrony-based models. SynCx outperforms or is strongly competitive with current models for unsupervised object discovery. SynCx also avoids certain systematic grouping errors of current models, such as the inability to separate similarly colored objects without additional supervision.

翻译：当前最先进的基于同步性的模型采用复值激活编码物体绑定，并在前馈架构中使用实值权重进行计算。我们论证了具有复值权重的循环架构在计算上的优势。我们提出了一种全卷积自编码器SynCx，它执行迭代约束满足：在每次迭代中，隐藏层瓶颈编码特定相位关系中特征的统计规律性配置；经过多次迭代，局部约束传播，模型收敛到相位分配的全局一致配置。绑定仅通过复值权重与激活之间的矩阵-向量乘积操作实现，无需当前基于同步性的模型中引入的额外机制。在无监督物体发现任务中，SynCx的性能优于或与当前模型具有显著竞争力。SynCx还避免了当前模型的某些系统性分组错误，例如在没有额外监督的情况下无法分离颜色相似的物体。