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Fully test-time adaptation aims to adapt the network model based onsequential analysis of input samples during the inference stage to address thecross-domain performance degradation problem of deep neural networks. We takeinspiration from the biological plausibility learning where the neuronresponses are tuned based on a local synapse-change procedure and activated bycompetitive lateral inhibition rules. Based on these feed-forward learningrules, we design a soft Hebbian learning process which provides an unsupervisedand effective mechanism for online adaptation. We observe that the performanceof this feed-forward Hebbian learning for fully test-time adaptation can besignificantly improved by incorporating a feedback neuro-modulation layer. Itis able to fine-tune the neuron responses based on the external feedbackgenerated by the error back-propagation from the top inference layers. Thisleads to our proposed neuro-modulated Hebbian learning (NHL) method for fullytest-time adaptation. With the unsupervised feed-forward soft Hebbian learningbeing combined with a learned neuro-modulator to capture feedback from externalresponses, the source model can be effectively adapted during the testingprocess. Experimental results on benchmark datasets demonstrate that ourproposed method can significantly improve the adaptation performance of networkmodels and outperforms existing state-of-the-art methods.

Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation