Utilizing unlabeled data in the target domain to perform continuous optimization is critical to enhance the generalization ability of neural networks. Most domain adaptation methods focus on time-consuming optimization of deep feature extractors, which limits the deployment on lightweight edge devices. Inspired by the memory mechanism and powerful generalization ability of biological neural networks in human brains, we propose a novel gradient-free Elastic Memory Network, namely EMN, to support quick fine-tuning of the mapping between features and prediction without heavy optimization of deep features. In particular, EMN adopts randomly connected neurons to memorize the association of features and labels, where the signals in the network are propagated as impulses, and the prediction is made by associating the memories stored on neurons based on their confidence. More importantly, EMN supports reinforced memorization of feature mapping based on unlabeled data to quickly adapt to a new domain. Experiments based on four cross-domain real-world datasets show that EMN can achieve up to 10% enhancement of performance while only needing less than 1% timing cost of traditional domain adaptation methods.

翻译：利用目标域的无标签数据进行持续优化对于提升神经网络的泛化能力至关重要。大多数域自适应方法侧重于对深度特征提取器进行耗时的优化，这限制了其在轻量级边缘设备上的部署。受人类大脑中生物神经网络的记忆机制和强大泛化能力启发，我们提出了一种无需梯度的新型弹性记忆网络（EMN），以支持在不进行深度特征繁重优化的前提下快速微调特征与预测之间的映射关系。具体而言，EMN采用随机连接的神经元来记忆特征与标签的关联，其中网络中的信号以脉冲形式传播，预测则通过基于置信度关联存储在神经元上的记忆来实现。更重要的是，EMN支持基于无标签数据对特征映射进行强化记忆，从而快速适应新域。基于四个跨域真实数据集的实验表明，EMN在仅需传统域自适应方法不足1%时间开销的情况下，可实现高达10%的性能提升。