Question: Do you use additional dataset OfficeHome?
Evidence: We additionally compare our proposed method with source-free UDA methods which adapts with entire unlabelled target dataset, including AdaBN, SHOT, SFDA and SDDA, as well as the few-shot method L 2 and FLUTE, in Table on Office and Office-Home. We observe that self-supervision over the entire unlabelled target can produce good adaptation performance. Despite using only 5 samples per class, finetuning LCCS has equal and better adaptation performance than source-free UDA methods in 5 out of 6 domain pairs on Office. SHOT outperforms in the more challenging OfficeHome dataset, which reflects the difficulty of source-free few-shot setting. However, our proposed method performs the best out of the few-shot methods evaluated, which demonstrates its effectiveness in the few-shot setting. We refer readers to Appendix Section C.4 for detailed results and comparisons with nonsource-free few-shot DA methods.
We compare the resources of the three source-free methods from different settings: SHOT, Tent, and our fewshot LCCS finetuning. From Table, the number and storage size of samples for our proposed method is much smaller than those used for SHOT. At test time, our method requires no computation for updating the model, which is at least on par with test-time adaptation methods since they can need additional backward passes for online adaptation. For instance, Tent needs at least an additional backward pass. More importantly, our setting is one way to address test-time adaptation's reliance on streaming conditions which may challenge its usefulness in practice.
We additionally compare our proposed method with source-free UDA methods which adapts with entire unlabelled target dataset, including AdaBN (Li et al., 2016), SHOT (Liang et al., 2020), SFDA (Kim et al., 2020) and SDDA (Kurmi et al., 2021), as well as the few-shot method L 2 and FLUTE, in Table 6 on Office and Office-Home. We observe that self-supervision over the entire unlabelled target can produce good adaptation performance. Despite using only 5 samples per class, finetuning LCCS has equal and better adaptation performance than source-free UDA methods in 5 out of 6 domain pairs on Office. SHOT outperforms in the more challenging OfficeHome dataset, which reflects the difficulty of source-free few-shot setting. However, our proposed method performs the best out of the few-shot methods evaluated, which demonstrates its effectiveness in the few-shot setting. We refer readers to Appendix Section C.4 for detailed results and comparisons with nonsource-free few-shot DA methods.
Answer: We have included a comparison of our proposed method with source-free UDA and source-free few-shot methods on OfficeHome in Table 6. SHOT (Liang et al., 2020) adapts with the entire target dataset and outperforms few-shot methods in the challenging OfficeHome dataset, which reflects the difficulty of the few-shot setting. However, our proposed method performs the best out of the few-shot methods evaluated, which demonstrates its effectiveness in this setting. On a smaller Office dataset in Table 6, our proposed method performs equal to and better than SHOT in 5 out of 6 domain pairs.