Distinct from human cognitive processing, deep neural networks trained by backpropagation can be easily fooled by adversarial examples. To design a semantically meaningful representation learning, we discard backpropagation, and instead, propose a local contrastive learning, where the representation for the inputs bearing the same label shrink (akin to boson) in hidden layers, while those of different labels repel (akin to fermion). This layer-wise learning is local in nature, being biological plausible. A statistical mechanics analysis shows that the target fermion-pair-distance is a key parameter. Moreover, the application of this local contrastive learning to MNIST benchmark dataset demonstrates that the adversarial vulnerability of standard perceptron can be greatly mitigated by tuning the target distance, i.e., controlling the geometric separation of prototype manifolds.

翻译：不同于人类认知处理，通过反向传播训练的深度神经网络容易被对抗样本轻易欺骗。为设计语义有意义的表征学习，我们摒弃反向传播，转而提出一种局部对比学习机制：在隐藏层中，相同标签的输入表征相互收缩（类似于玻色子），而不同标签的表征相互排斥（类似于费米子）。这种逐层学习本质上具有局部性和生物可解释性。统计力学分析表明，目标费米子对间距是关键参数。此外，将该局部对比学习应用于MNIST基准数据集的结果表明，通过调节目标距离（即控制原型流形的几何分离度），可大幅缓解标准感知器的对抗脆弱性。