AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation
Damian Sójka Sebastian Cygert Bartłomiej Twardowski Tomasz Trzciński
Test-time adaptation is a promising research direction that allows the source
model to adapt itself to changes in data distribution without any supervision.
Yet, current methods are usually evaluated on benchmarks that are only a
simplification of real-world scenarios. Hence, we propose to validate test-time
adaptation methods using the recently introduced datasets for autonomous
driving, namely CLAD-C and SHIFT. We observe that current test-time adaptation
methods struggle to effectively handle varying degrees of domain shift, often
resulting in degraded performance that falls below that of the source model. We
noticed that the root of the problem lies in the inability to preserve the
knowledge of the source model and adapt to dynamically changing, temporally
correlated data streams. Therefore, we enhance well-established self-training
framework by incorporating a small memory buffer to increase model stability
and at the same time perform dynamic adaptation based on the intensity of
domain shift. The proposed method, named AR-TTA, outperforms existing
approaches on both synthetic and more real-world benchmarks and shows
robustness across a variety of TTA scenarios.