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Shuai Lü

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9 papers

1 author row

AAAI Conference 2026 Conference Paper

Prototype-Driven Active Domain Adaptation with Density Consideration

Zeyu Zhang
Chun Shen
Qiang Ma
Meng Kang
Shuai Lü

Active domain adaptation (ADA) aims to select a small set of target samples for annotation and use them for training to maximally boost the adaptation performance. However, most existing ADA methods only rely on the original output of the model, without considering the relationship between the source and target domain features, which may lead to selecting uninformative samples. In this paper, we propose an effective ADA framework: Prototype-Driven Active Domain Adaptation with density consideration (PDADA). It selects the most valuable target samples in the presence of domain shift through two criteria: Density-Conscious Domainness (DCD) and Prototype-Driven Informativeness (PDI). Furthermore, considering the class imbalance and cluster looseness issues in sample selection and domain adaptation, we develop a Class Balanced Expansion (CBE) algorithm and the Adversarial Active Domain Adaptation via Protecting Structured Information (AADA-PSI). Extensive experiments demonstrate that under the cooperation of the above components, PDADA outperforms previous methods on several challenging benchmarks and can be generalized to multi-source active domain adaptation setting.

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AAAI Conference 2026 Conference Paper

State Proficiency-Based Adaptive Fine-Tuning for Offline-to-Online Reinforcement Learning

Songlin Li
Wei Xiao
Hao Wu
Xiaodan Zhang
Daolong An
Shuai Lü

In offline-to-online (O2O) reinforcement learning, achieving efficient performance improvement while maintaining training stability remains a critical challenge for effective fine-tuning. Existing O2O methods usually focus on the balance between policy improvement and policy constraint during online fine-tuning. However, they often overlook sample differences, leading to suboptimal performance. To address this challenge, we identify that the effectiveness of policy learning exhibits significant variation across states. Therefore, we propose the notion of state proficiency to capture the degree of effective learning in a given state. We propose State Proficiency-Based Adaptive Fine-Tuning (SPA), a straightforward yet effective method that establishes proficiency-based sample priorities in policy optimization to facilitate effective fine-tuning. Specifically, SPA focuses on low proficiency samples during policy improvement to enhance sample efficiency, while emphasizing high proficiency samples during policy constraint to ensure stable training. Extensive empirical results demonstrate that SPA achieves significant improvements over existing methods, attaining state-of-the-art performance on the D4RL benchmark.

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NeurIPS Conference 2025 Conference Paper

Domain Adaptive Hashing Retrieval via VLM Assisted Pseudo-Labeling and Dual Space Adaptation

Jingyao Li
Zhanshan Li
Shuai Lü

Unsupervised domain adaptive hashing has emerged as a promising approach for efficient and memory-friendly cross-domain retrieval. It leverages the model learned on labeled source domains to generate compact binary codes for unlabeled target domain samples, ensuring that semantically similar samples are mapped to nearby points in the Hamming space. Existing methods typically apply domain adaptation techniques to the feature space or the Hamming space, especially pseudo-labeling and feature alignment. However, the inherent noise of pseudo-labels and the insufficient exploration of complementary knowledge across spaces hinder the ability of the adapted model. To address these challenges, we propose a Vision-language model assisted Pseudo-labeling and Dual Space adaptation (VPDS) method. Motivated by the strong zero-shot generalization capabilities of pre-trained vision-language models (VLMs), VPDS leverages VLMs to calibrate pseudo-labels, thereby mitigating pseudo-label bias. Furthermore, to simultaneously utilize the semantic richness of high-dimensional feature space and preserve discriminative efficiency of low-dimensional Hamming space, we introduce a dual space adaptation approach that performs independent alignment within each space. Extensive experiments on three benchmark datasets demonstrate that VPDS consistently outperforms existing methods in both cross-domain and single-domain retrieval tasks, highlighting its effectiveness and superiority.

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IJCAI Conference 2024 Conference Paper

Alleviating Imbalanced Pseudo-label Distribution: Self-Supervised Multi-Source Domain Adaptation with Label-specific Confidence

Shuai Lü
Meng Kang
Ximing Li

The existing self-supervised Multi-Source Domain Adaptation (MSDA) methods often suffer an imbalanced characteristic among the distribution of pseudo-labels. Such imbalanced characteristic results in many labels with too many or too few pseudo-labeled samples on the target domain, referred to as easy-to-learn label and hard-to-learn label, respectively. Both of these labels hurt the generalization performance on the target domain. To alleviate this problem, in this paper we propose a novel multi-source domain adaptation method, namely Self-Supervised multi-Source Domain Adaptation with Label-specific Confidence (S3DA-LC). Specifically, we estimate the label-specific confidences, i. e. , the learning difficulties of labels, and adopt them to generate the pseudo-labels for target samples, enabling to simultaneously constrain and enrich the pseudo supervised signals for easy-to-learn and hard-to-learn labels. We evaluate S3DA-LC on several benchmark datasets, indicating its superior performance compared with the existing MSDA baselines.

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AAAI Conference 2024 Conference Paper

Double Buffers CEM-TD3: More Efficient Evolution and Richer Exploration

Sheng Zhu
Chun Shen
Shuai Lü
Junhong Wu
Daolong An

CEM-TD3 is a combination scheme using the simple cross-entropy method (CEM) and Twin Delayed Deep Deterministic policy gradient (TD3), and it achieves a satisfactory trade-off between performance and sample efficiency. However, we find that CEM-TD3 cannot fully address the low efficiency of policy search caused by CEM, and the policy gradient learning introduced by TD3 will weaken the diversity of individuals in the population. In this paper, we propose Double Buffers CEM-TD3 (DBCEM-TD3) that optimizes both CEM and TD3. For CEM, DBCEM-TD3 maintains an actor buffer to store the population required for evolution. In each iteration, it only needs to generate a small number of actors to replace the poor actors in the policy buffer to achieve more efficient evolution. The fitness of individuals in the actor buffer decreases exponentially with time, which can avoid premature convergence of the mean actor. For TD3, DBCEM-TD3 maintains a critic buffer with the same number of critics as the number of actors generated in each iteration, and each critic is trained independently by sampling from the shared replay buffer. In each iteration, each newly generated actor uses different critics to guide learning. This ensures more diverse behaviors among the learned actors, enabling richer experiences to be collected during the evaluation phase. We conduct experimental evaluations on five continuous control tasks provided by OpenAI Gym. DBCEM-TD3 outperforms CEM-TD3, TD3, and other classic off-policy reinforcement learning algorithms in terms of performance and sample efficiency.

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AAAI Conference 2024 Conference Paper

Low Category Uncertainty and High Training Potential Instance Learning for Unsupervised Domain Adaptation

Xinyu Zhang
Meng Kang
Shuai Lü

Recently, instance contrastive learning achieves good results in unsupervised domain adaptation. It reduces the distances between positive samples and the anchor, increases the distances between negative samples and the anchor, and learns discriminative feature representations for target samples. However, most recent methods for identifying positive and negative samples are based on whether the pseudo-labels of samples and the pseudo-label of the anchor correspond to the same class. Due to the lack of target labels, many uncertain data are mistakenly labeled during the training process, and many low training potential data are also utilized. To address these problems, we propose Low Category Uncertainty and High Training Potential Instance Learning for Unsupervised Domain Adaptation (LUHP). We first propose a weight to measure the category uncertainty of the target sample. We can effectively filter the samples near the decision boundary through category uncertainty thresholds which are calculated by weights. Then we propose a new loss to focus on samples with high training potential. Finally, for anchors with low category uncertainty, we propose a sample reuse strategy to make the model more robust. We demonstrate the effectiveness of LUHP by showing the results of four datasets widely used in unsupervised domain adaptation.

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IJCAI Conference 2024 Conference Paper

Reconfigurability-Aware Selection for Contrastive Active Domain Adaptation

Zeyu Zhang
Chun Shen
Shuai Lü
Shaojie Zhang

Active domain adaptation (ADA) aims to label a small portion of target samples to drastically improve the adaptation performance. The existing ADA methods mostly rely on the output of domain discriminator or the original prediction probability to design sample selection strategies and do not fully explore the semantic information of source and target domain features, which may lead to selecting the valueless target samples. Moreover, most of them require complex network structures (such as introducing additional domain discriminator, multiple classifiers, or loss predictors) and multiple query functions. In this work, we propose a concise but effective ADA method called Reconfigurability-Aware Selection for Contrastive active domain adaptation (RASC). With the reconfigurability-aware sample selection strategy, RASC can select the most valuable target samples for annotation in the presence of domain shift. To better utilize the selected target samples, we further design a contrastive learning-based gradual active domain adaptation framework. In addition, we propose a variant of RASC called RASC-Ob, which uses a simpler sample annotation method and supplements the learning of misclassified samples. Extensive experimental results on multiple benchmarks demonstrate the superiority of RASC.

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AAAI Conference 2024 Conference Paper

Reviewing the Forgotten Classes for Domain Adaptation of Black-Box Predictors

Shaojie Zhang
Chun Shen
Shuai Lü
Zeyu Zhang

For addressing the data privacy and portability issues of domain adaptation, Domain Adaptation of Black-box Predictors (DABP) aims to adapt a black-box source model to an unlabeled target domain without accessing both the source-domain data and details of the source model. Although existing DABP approaches based on knowledge distillation (KD) have achieved promising results, we experimentally find that these methods all have the minority class forgetting issue, which refers that the trained model completely forgets some minority classes. To address this issue, we propose a method called Reviewing the Forgotten Classes (RFC), which including two main modules. Firstly, we propose a simple but effective component called selection training (ST). ST selects classes that the model tends to forget according to the learning status of the model and obtains clean samples of the selected classes with the small-loss criterion for enhanced training. ST is orthogonal to previous methods and can effectively alleviate their minority class forgetting issue. Secondly, we find that neighborhood clustering (NC) can help the model learn more balanced than KD so that further alleviate the minority class forgetting issue. However, NC is based on the fact that target features from the source model already form some semantic structure, while DABP is unable to obtain the source model. Thus, we use KD and ST to warm up the target model to form a certain semantic structure. Overall, our method inherits the merits of both ST and NC, and achieves state of the art on three DABP benchmarks.

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IJCAI Conference 2023 Conference Paper

Adaptive Estimation Q-learning with Uncertainty and Familiarity

Xiaoyu Gong
Shuai Lü
Jiayu Yu
Sheng Zhu
Zongze Li

One of the key problems in model-free deep reinforcement learning is how to obtain more accurate value estimations. Current most widely-used off-policy algorithms suffer from over- or underestimation bias which may lead to unstable policy. In this paper, we propose a novel method, Adaptive Estimation Q-learning (AEQ), which uses uncertainty and familiarity to control the value estimation naturally and can adaptively change for specific state-action pair. We theoretically prove the property of our familiarity term which can even keep the expected estimation bias approximate to 0, and experimentally demonstrate our dynamic estimation can improve the performance and prevent the bias continuously increasing. We evaluate AEQ on several continuous control tasks, outperforming state-of-the-art performance. Moreover, AEQ is simple to implement and can be applied in any off-policy actor-critic algorithm.

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