Few-Shot Adaptation of Pre-Trained Networks for Domain Shift

Deep networks are prone to performance degradation To carry out this adaptation, a range of methods with varying when there is a domain shift between the requirements on the availability of source and target domain source (training) data and target (test) data. Recent data have been developed. In the classic domain adaptation test-time adaptation methods update batch normalization (DA) setting, methods assume source and target data are layers of pre-trained source models deployed jointly available for training [Wilson and Cook, 2020], which in new target environments with streaming compromises the privacy of source domain data. To address data to mitigate such performance degradation. Although this, methods for the source-free DA setting [Qiu et al., 2021; such methods can adapt on-the-fly without Yang et al., 2021; Liang et al., 2020] instead adapt a pretrained first collecting a large target domain dataset, source model using only unlabeled target data, but their performance is dependent on streaming conditions they still require access to the entire unlabeled target dataset such as mini-batch size and class-distribution, like traditional DA methods. This can delay adaptation, or which can be unpredictable in practice. In this even make it impractical, when collecting the unlabeled target work, we propose a framework for few-shot domain data is costly in terms of time or other resources.