Fully test-time adaptation aims to adapt the network model based on
sequential analysis of input samples during the inference stage to address the
cross-domain performance degradation problem of deep neural networks. We take
inspiration from the biological plausibility learning where the neuron
responses are tuned based on a local synapse-change procedure and activated by
competitive lateral inhibition rules. Based on these feed-forward learning
rules, we design a soft Hebbian learning process which provides an unsupervised
and effective mechanism for online adaptation. We observe that the performance
of this feed-forward Hebbian learning for fully test-time adaptation can be
significantly improved by incorporating a feedback neuro-modulation layer. It
is able to fine-tune the neuron responses based on the external feedback
generated by the error back-propagation from the top inference layers. This
leads to our proposed neuro-modulated Hebbian learning (NHL) method for fully
test-time adaptation. With the unsupervised feed-forward soft Hebbian learning
being combined with a learned neuro-modulator to capture feedback from external
responses, the source model can be effectively adapted during the testing
process. Experimental results on benchmark datasets demonstrate that our
proposed method can significantly improve the adaptation performance of network
models and outperforms existing state-of-the-art methods.

本文研究了深度神经网络领域中跨域性能退化问题，提出了一种基于完全测试时间自适应方法来提高网络适应性能的方法，其中采用了软式 Hebbian 学习过程和反馈神经调节层来辅助在线适应。实验结果表明，该方法可以显著改善网络模型的适应性能并超越现有的最新方法。