论文标题
与监督学习的对比相似性匹配
Contrastive Similarity Matching for Supervised Learning
论文作者
论文摘要
我们提出了一种新颖的生物学知识解决方案，以解决由腹侧视觉途径和训练深度神经网络的观察到的信用分配问题。在这两种情况下，同一类别中对象的表示逐渐变得更加相似，而属于不同类别的对象变得较不相似。我们使用此观察结果来激发深层网络中特定层的学习目标：每个层都旨在学习一个代表性相似性矩阵，该矩阵在以前的层和后来的层之间进行了插值。我们使用对比的相似性匹配的目标函数来提出这一想法，并从其深层神经网络具有前馈，侧向和反馈连接，以及表现出具有生物学性质的HEBBIAN和抗Hebbian可塑性的神经元。对比性相似性匹配可以解释为一种基于能量的学习算法，但与其他人在构建对比功能方面有显着差异。
We propose a novel biologically-plausible solution to the credit assignment problem motivated by observations in the ventral visual pathway and trained deep neural networks. In both, representations of objects in the same category become progressively more similar, while objects belonging to different categories become less similar. We use this observation to motivate a layer-specific learning goal in a deep network: each layer aims to learn a representational similarity matrix that interpolates between previous and later layers. We formulate this idea using a contrastive similarity matching objective function and derive from it deep neural networks with feedforward, lateral, and feedback connections, and neurons that exhibit biologically-plausible Hebbian and anti-Hebbian plasticity. Contrastive similarity matching can be interpreted as an energy-based learning algorithm, but with significant differences from others in how a contrastive function is constructed.
