Dynamic Routing Between Capsules(Hinton)

本文介绍了一种名为胶囊网络的新架构,该网络通过使用一组称为胶囊的神经元来表示特定实体(如对象或对象部分)的实例化参数。胶囊网络在MNIST数据集上实现了最先进的性能,并且在高度重叠的数字识别任务中明显优于卷积神经网络。通过迭代路由协议,低级胶囊能够预测高级胶囊的活动向量。

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ABSTRACT

A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation paramters. Active capsules at one level make predictions, via transformation matrices, for the instantiation parameters of higher-level capsules. When multiple predictions agree, a higher level capsule becomes active. We show that a discrimininatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits. To achieve these results we use an iterative routing-by-agreement mechanism: A lower-level capsule prefers to send its output to higher level capsules whose activity vectors have a big scalar product with the prediction coming from the lower-level capsule. The final version of the paper is under revision to encorporate reviewers comments.

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