arXiv:2607. 20367v1 Announce Type: new Abstract: Correlation alignment and the maximum mean discrepancy are two widely used distribution-matching frameworks for unsupervised domain adaptation (UDA).
By Andrea Napoli
The paper introduces Joint Distribution Alignment for Universal Domain Adaptation (JAUA), a new algorithm designed for scenarios where source and target domains have differing label spaces. It provides a theoretical upper bound on generalization error for Universal Domain Adaptation and proposes aligning joint distributions using Chi‑Square divergence, complemented by a progressive pseudo‑labeling strategy. Experiments on six public image datasets show JAUA outperforms existing methods in handling Universal Domain Adaptation challenges.
By Shizhe Li, Hongshan Pu, Mengying Xie, Yi Xiang, Xiaowei Yang
arXiv:2607. 20374v1 Announce Type: new Abstract: This paper studies the problem of stochastic variance reduction (SVR) for the maximum mean discrepancy (MMD) and correlation alignment (CORAL) loss functions.
By Andrea Napoli
arXiv:2407. 21311v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data.
By Ali Abedi, Q. M. Jonathan Wu, Ning Zhang, Farhad Pourpanah
arXiv:2601. 08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data.
By Matina Mahdizadeh Sani, Nima Jamali, Mohammad Jalali, Farzan Farnia
arXiv:2610.01890v1 Announce Type: cross
Abstract: Deep generative networks have recently achieved unprecedented performance in precise image and video editing using sophisticated textual prompts. How...
By Victor Enescu, Assaad Zeghina, Matthieu Meignin, Nicolas Viltard, C\'ecile Mallet