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Chuan-Xian Ren

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9 papers

1 author row

AAAI Conference 2026 Conference Paper

GCA: Geometry-aware Conditional Alignment for Partial Domain Adaptation with Coding Rate Reduction

Xiaohui Chen
Chuan-Xian Ren

Partial Domain Adaptation (PDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain, where the target label space is a subset of the source label space. In PDA scenario, existing methods typically achieve transferability through distribution alignment in a statistical framework, and discriminability through geometric modeling. These two aspects are often treated as separate frameworks, which severs the intrinsic connection between them. To bridge this gap, we propose a unified framework termed Geometry-aware Conditional Alignment (GCA), which is derived from theoretical insights of Maximum Coding Rate Reduction. GCA collaboratively achieves conditional alignment and orthogonal discriminability in a unified framework, making the learned features more interpretable in both statistical and geometric aspects. As a result, GCA effectively enhances both the transferability and discriminability of features. Extensive experiments on four benchmark datasets validate the effectiveness of GCA.

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AAAI Conference 2026 Conference Paper

Inverse Optimal Transport for Efficient Adaptation of Vision-Language Models

Shupeng Qiu
Chuan-Xian Ren

Vision–language models (VLMs) such as CLIP have unlocked powerful zero-shot transfer, yet efficient adaptation to downstream tasks remains challenging. Existing methods often depend on graph structures and dataset-specific tuning, making them sensitive to modality gaps and computationally costly at scale. In this paper, we propose IOTA (Inverse Optimal Transport Adaptation), a lightweight algorithm that reformulates VLMs inference from the perspective of inverse optimal transport (IOT), providing a unified view of training and inference. Under the IOT framework, IOTA enhances zero-shot alignment via a theory-guided unbalanced OT strategy and refines textual prototypes using OT-based pseudo-labels with a marginal-aware adaptive threshold, enabling reliable supervision without gradient updates. The framework naturally extends to few-shot scenarios through a label-guided masking mechanism. By decoupling image–text interactions from other inter-modal dependencies, IOTA avoids task-specific tuning and expensive affinity construction. Extensive experiments on standard benchmarks show that IOTA consistently improves zero-shot and few-shot performance while reducing memory and computation overhead, validating both its theoretical insight and plug-and-play practicality.

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AAAI Conference 2026 Conference Paper

Wasserstein-Aware Transfer: Class-Level Alignment for Robust Diffusion Model Adaptation

Zixian Huang
Chuan-Xian Ren

Diffusion models have achieved impressive generative performance across diverse domains such as image, video, and scientific data generation. However, fine-tuning these models for new tasks remains challenging due to their large scale, architectural diversity, and high sensitivity to hyperparameters—particularly learning rates. In this work, we propose Wasserstein-Aware Transfer (WAT), a principled and effective fine-tuning strategy grounded in diffusion trajectory analysis and optimal transport theory. Our key insight is that the distributional discrepancies between diffusion trajectories from different datasets decrease progressively over time and converge near the noise end. Based on this observation, we introduce a class-wise matching mechanism that minimizes the Wasserstein distance between class distributions of source and target datasets. This enables alignment at the class level without modifying the standard fine-tuning pipeline. To further enhance knowledge retention, we propose a novel sampling strategy that linearly combines class-conditional outputs from both pretrained and fine-tuned models. This method is simple yet effective, requiring negligible computational overhead while preserving domain-specific and generalizable knowledge. Extensive experiments across seven diverse benchmarks demonstrate that WAT reliably enhances generation quality under distribution shifts, outperforming competitive baselines. These results underscore its robustness and affirm the potential of optimal transport as a principled basis for knowledge transfer in diffusion models.

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NeurIPS Conference 2025 Conference Paper

A Generalized Label Shift Perspective for Cross-Domain Gaze Estimation

Hao-Ran Yang
Xiaohui Chen
Chuan-Xian Ren

Aiming to generalize the well-trained gaze estimation model to new target domains, Cross-domain Gaze Estimation (CDGE) is developed for real-world application scenarios. Existing CDGE methods typically extract the domain-invariant features to mitigate domain shift in feature space, which is proved insufficient by Generalized Label Shift (GLS) theory. In this paper, we introduce a novel GLS perspective to CDGE and modelize the cross-domain problem by label and conditional shift problem. A GLS correction framework is presented and a feasible realization is proposed, in which a importance reweighting strategy based on truncated Gaussian distribution is introduced to overcome the continuity challenges in label shift correction. To embed the reweighted source distribution to conditional invariant learning, we further derive a probability-aware estimation of conditional operator discrepancy. Extensive experiments on standard CDGE tasks with different backbone models validate the superior generalization capability across domain and applicability on various models of proposed method.

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NeurIPS Conference 2025 Conference Paper

A Physics-preserved Transfer Learning Method for Differential Equations

Hao-Ran Yang
Chuan-Xian Ren

While data-driven methods such as neural operator have achieved great success in solving differential equations (DEs), they suffer from domain shift problems caused by different learning environments (with data bias or equation changes), which can be alleviated by transfer learning (TL). However, existing TL methods adopted in DEs problems lack either generalizability in general DEs problems or physics preservation during training. In this work, we focus on a general transfer learning method that adaptively correct the domain shift and preserve physical relation within the equation. Mathematically, we characterize the data domain as product distribution and the essential problems as distribution bias and operator bias. A Physics-preserved Optimal Tensor Transport (POTT) method that simultaneously admits generalizability to common DEs and physics preservation of specific problem is proposed to adapt the data-driven model to target domain, utilizing the pushforward distribution induced by the POTT map. Extensive experiments in simulation and real-world datasets demonstrate the superior performance, generalizability and physics preservation of the proposed POTT method.

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AAAI Conference 2024 Conference Paper

Probability-Polarized Optimal Transport for Unsupervised Domain Adaptation

Yan Wang
Chuan-Xian Ren
Yi-Ming Zhai
You-Wei Luo
Hong Yan

Optimal transport (OT) is an important methodology to measure distribution discrepancy, which has achieved promising performance in artificial intelligence applications, e.g., unsupervised domain adaptation. However, from the view of transportation, there are still limitations: 1) the local discriminative structures for downstream tasks, e.g., cluster structure for classification, cannot be explicitly admitted by the learned OT plan; 2) the entropy regularization induces a dense OT plan with increasing uncertainty. To tackle these issues, we propose a novel Probability-Polarized OT (PPOT) framework, which can characterize the structure of OT plan explicitly. Specifically, the probability polarization mechanism is proposed to guide the optimization direction of OT plan, which generates a clear margin between similar and dissimilar transport pairs and reduces the uncertainty. Further, a dynamic mechanism for margin is developed by incorporating task-related information into the polarization, which directly captures the intra/inter class correspondence for knowledge transportation. A mathematical understanding for PPOT is provided from the view of gradient, which ensures interpretability. Extensive experiments on several datasets validate the effectiveness and empirical efficiency of PPOT.

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IJCAI Conference 2024 Conference Paper

Rethinking Correlation Learning via Label Prior for Open Set Domain Adaptation

Zi-Xian Huang
Chuan-Xian Ren

Open Set Domain Adaptation (OSDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain, where known classes exist across domains while unknown classes are present only in the target domain. Existing methods rely on the clustering structure to identify the unknown classes, which empirically induces a large identification error if the unknown classes are a mixture of multiple components. To break through this barrier, we formulate OSDA from the view of correlation and propose a correlation metric-based framework called Balanced Correlation Learning (BCL). BCL employs Hilbert-Schmidt Independence Criterion (HSIC) to characterize the separation between unknown and known classes, where HSIC is reformulated as the nodes’ relation on graph. By considering the label prior as variable, theoretical results are derived to analytically show a sufficient condition for desired learning direction for OSDA. Methodologically, the class-balanced HSIC is proposed to preserve domain-invariant and class-discriminative features. With the guarantee of correlation learning, the entropy-based principle can effectively identify the unknown classes via uncertainty. Empirically, extensive evaluations are conducted, where BCL achieves significant performance improvements.

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AAAI Conference 2023 Conference Paper

Adaptive Texture Filtering for Single-Domain Generalized Segmentation

Xinhui Li
Mingjia Li
Yaxing Wang
Chuan-Xian Ren
Xiaojie Guo

Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features. Existing methods diversify images in the source domain by adding complex or even abnormal textures to reduce the sensitivity to domain-specific features. However, these approaches depends heavily on the richness of the texture bank and training them can be time-consuming. In contrast to importing textures arbitrarily or augmenting styles randomly, we focus on the single source domain itself to achieve the generalization. In this paper, we present a novel adaptive texture filtering mechanism to suppress the influence of texture without using augmentation, thus eliminating the interference of domain-specific features. Further, we design a hierarchical guidance generalization network equipped with structure-guided enhancement modules, which purpose to learn the domain-invariant generalized knowledge. Extensive experiments together with ablation studies on widely-used datasets are conducted to verify the effectiveness of the proposed model, and reveal its superiority over other state-of-the-art alternatives.

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AAAI Conference 2020 Conference Paper

Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment

You-Wei Luo
Chuan-Xian Ren
Pengfei Ge
Ke-Kun Huang
Yu-Feng Yu

Unsupervised domain adaptation is effective in leveraging the rich information from the source domain to the unsupervised target domain. Though deep learning and adversarial strategy make an important breakthrough in the adaptability of features, there are two issues to be further explored. First, the hard-assigned pseudo labels on the target domain are risky to the intrinsic data structure. Second, the batchwise training manner in deep learning limits the description of the global structure. In this paper, a Riemannian manifold learning framework is proposed to achieve transferability and discriminability consistently. As to the ﬁrst problem, this method establishes a probabilistic discriminant criterion on the target domain via soft labels. Further, this criterion is extended to a global approximation scheme for the second issue; such approximation is also memory-saving. The manifold metric alignment is exploited to be compatible with the embedding space. A theoretical error bound is derived to facilitate the alignment. Extensive experiments have been conducted to investigate the proposal and results of the comparison study manifest the superiority of consistent manifold learning framework.

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