Abstract
Universal domain adaptation (UniDA) aims to transfer the knowledge of common classes from source domain to target domain without any prior knowledge on the label set, which requires to distinguish the unknown samples from the known ones in the target domain. Like the traditional unsupervised domain adaptation problem, the misalignment between two domains exists due to the biased and less-discriminative embedding. Recent methods proposed to complete the domain misalignment by clustering target samples with the nearest neighbors or the prototypes. However, it is dangerous to do so since we do not have any prior knowledge about the distributions of unknown samples which can magnify the misalignment especially when the unknown set is big. Meanwhile, other existing classifier-based methods could easily produce overconfident predictions of unknown samples because of the supervised objective in source domain leading the whole model to be biased towards the common classes in the target domain. Therefore, we propose a novel non-parameter unknown samples detection method based on mapping the samples in the original feature space into a reliable linear sub-space which makes data points more sparse to reduce the misalignment between unknown samples and source samples. Moreover, unlike the recent methods applying extra parameters to improve the classification of unknown samples, this paper well balances the confidence values of both known and unknown samples through an unknown-adaptive margin loss which can control the gradient updating of the classifier learning on supervised source samples depending on the confidence level of detected unknown samples at current step. Finally, experiments on four public datasets demonstrate that our method significantly outperforms existing state-of-the-art methods.
Abstract (translated)
URL
https://arxiv.org/abs/2209.09616
