A Simple Signal for Domain Shift

Goirik Chakrabarty, Manogna Sreenivas, Soma Biswas; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023, pp. 3577-3584

Test time domain adaptation has come to the forefront as a challenging scenario in recent times. Although single domain test-time adaptation has been well studied and shown impressive performance, this can be limiting when the model is deployed in a dynamic test environment. We explore this continual domain test time adaptation problem here. Specifically, we question if we can translate the effectiveness of single domain adaptation methods to continuous test-time adaptation scenario. We take a step towards bridging the gap between these two settings by proposing a domain shift detection mechanism and hence allowing us to employ the current test-time adaptation methods even in a continual setting. We propose to use the given source domain trained model to continually measure the similarity between the feature representations of the consecutive batches. A domain shift is detected when this measure crosses a certain threshold, which we use as a trigger to reset the model back to source and continue test-time adaptation. We demonstrate the effectiveness of our method by performing experiments across datasets, batch sizes and different single domain test-time adaptation baselines.