NeurIPS 2025

DUET: Dual-Perspective Pseudo Labeling and
Uncertainty-aware Exploration & Exploitation
Training for Source-Free Domain Adaptation
Jae Yun Lee1
JaeHyeon Park2,
Gyoomin Lee2,
Bogyeong Kim3,
Min Hee Cha3,
Hyeok Nam1,
Joo Hyeon Jeon1,
Hyunse Lee2,
Sung In Cho1∗
1 Department of Artificial Intelligence at Sogang University
35 Baekbeom-ro, Mapo-gu, Seoul 04107, Republic of Korea
{stcaft, skagur10, jhbox7, csi2267}@sogang.ac.kr
2,3 Department of Computer Science and Artificial Intelligence at Dongguk University
30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
2{pjh0011, exceleaf, sae4394}@dongguk.edu
3{qhruddl51, rninicha}@dgu.ac.kr
Abstract
Source-free domain adaptation (SFDA) aims to adapt a pre-trained source model
to an unlabeled target domain without requiring labeled source data. In a self-
supervised setting, relying on pseudo labels on target domain samples facilitates
the domain adaptation performance providing strong supervision. However, a crit-
ical problem of this approach is the inherent instability of the pre-trained source
model in the target domain, leading to unreliable pseudo labels for the target do-
main data. To tackle this, we propose a novel Dual-perspective pseudo labeling
strategy that jointly leverages a task-specific perspective and a domain-invariant
perspective, assigning pseudo labels only to target samples on which the target
model’s predictions and CLIP’s predictions agree. To further enhance representa-
tion learning without introducing noisy supervision, we apply consistency training
to uncertain samples. Additionally, we introduce a Tsallis mutual information
(TMI)-based vision optimization strategy guided by an Uncertainty-based adapta-
tion index (UAI), which dynamically modulates entropy sensitivity based on the
model’s adaptation uncertainty. The UAI-based training paradigm enables stable
and adaptive domain alignment by effectively balancing exploration and exploita-
tion processes during the optimization process. Our pr...