Papers › Upcycling Models under Domain and Category Shift
Upcycling Models under Domain and Category Shift
Sanqing Qu, Tianpei Zou, Florian Roehrbein, Cewu Lu, Guang Chen, DaCheng Tao, Changjun Jiang
Deep neural networks (DNNs) often perform poorly in the presence of domain shift and category shift. How to upcycle DNNs and adapt them to the target task remains an important open problem. Unsupervised Domain Adaptation (UDA), especially recently proposed Source-free Domain Adaptation (SFDA), has become a promising technology to address this issue. Nevertheless, existing SFDA methods require that the source domain and target domain share the same label space, consequently being only applicable to the vanilla closed-set setting. In this paper, we take one step further and explore the Source-free Universal Domain Adaptation (SF-UniDA). The goal is to identify "known" data samples under both domain and category shift, and reject those "unknown" data samples (not present in source classes), with only the knowledge from standard pre-trained source model. To this end, we introduce an innovative global and local clustering learning technique (GLC). Specifically, we design a novel, adaptive one-vs-all global clustering algorithm to achieve the distinction across different target classes and introduce a local k-NN clustering strategy to alleviate negative transfer. We examine the superiority of our GLC on multiple benchmarks with different category shift scenarios, including partial-set, open-set, and open-partial-set DA. Remarkably, in the most challenging open-partial-set DA scenario, GLC outperforms UMAD by 14.8\% on the VisDA benchmark. The code is available at https://github.com/ispc-lab/GLC.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Universal Domain Adaptation | DomainNet | GLC | H-Score | 55.1 | #3 of 12 | Archive leaderboard | report |
| Universal Domain Adaptation | DomainNet | GLC | Source-free | yes | #3 of 12 | Archive leaderboard | report |
| Universal Domain Adaptation | Office-31 | GLC | H-score | 87.8 | #5 of 12 | Archive leaderboard | report |
| Universal Domain Adaptation | Office-31 | GLC | Source-Free | yes | #5 of 12 | Archive leaderboard | report |
| Universal Domain Adaptation | Office-Home | GLC | H-Score | 75.6 | #6 of 14 | Archive leaderboard | report |
| Universal Domain Adaptation | Office-Home | GLC | Source-free | yes | #6 of 14 | Archive leaderboard | report |
| Universal Domain Adaptation | Office-Home | GLC | VLM | no | #6 of 14 | Archive leaderboard | report |
| Universal Domain Adaptation | VisDA2017 | GLC | H-score | 73.1 | #3 of 13 | Archive leaderboard | report |
| Universal Domain Adaptation | VisDA2017 | GLC | Source-free | yes | #3 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections