Search Results for author:
Found 3 papers, 1 papers with code
VLLaVO: Mitigating Visual Gap through LLMs
Recent advances achieved by deep learning models rely on the independent and identically distributed assumption, hindering their applications in real-world scenarios with domain shifts.
Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation
However, the performance of existing UDA methods is constrained by the large domain shift and limited target domain data.
Diffusion-based Target Sampler for Unsupervised Domain Adaptation
The generated samples can well simulate the data distribution of the target domain and help existing UDA methods transfer from the source domain to the target domain more easily, thus improving the transfer performance.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
