Title: Self-Training with Dynamic Weighting for Robust Gradual Domain Adaptation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Gradual Domain Adaptation

Adversarial Domain Adaptation

Distribution Shift

Out-of-distribution Generalization
Abstract: In this paper, we propose a new method called \textit{Self-Training with Dynamic Weighting} (STDW), which aims to enhance robustness in Gradual Domain Adaptation (GDA) by addressing the challenge of smooth knowledge migration from the source to the target domain. Traditional GDA methods mitigate domain shift through intermediate domains and self-training but often suffer from inefficient knowledge migration or incomplete intermediate data. Our approach introduces a dynamic weighting mechanism that adaptively balances the loss contributions of the source and target domains during training. Specifically, we design an optimization framework governed by a time-varying hyperparameter $\varrho$ (progressing from 0 to 1), which controls the strength of domain-specific learning and ensures stable adaptation. The method leverages self-training to generate pseudo-labels and optimizes a weighted objective function for iterative model updates, maintaining robustness across intermediate domains. Experiments on rotated MNIST, color-shifted MNIST, portrait datasets, and the Cover Type dataset demonstrate that STDW outperforms existing baselines. Ablation studies further validate the critical role of $\varrho$'s dynamic scheduling in achieving progressive adaptation, confirming its effectiveness in reducing domain bias and improving generalization. This work provides both theoretical insights and a practical framework for robust gradual domain adaptation, with potential applications in dynamic real-world scenarios.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors propose a novel gradual self-training method named Generalized Dynamic Osmosis (STDW), which largely improves the performance of the model on the gradual domain adaptation problem.\n\nThe method involves three innovations. First, instead of assigning labels for the next whole domain and keeping them unchanged when fine-tuning, STDW uses a batchwise strategy of predicting pseudo-labels for a mini-batch of data so the pseudo-labels are consistently updated by a new model. Second, they use cyclic batch matching to facilitate robust inter-domain adaptation. Third, a weighted loss with a dynamic weight is adopted to stabilize the training process.\n\nThe authors conduct some experiments to validate the efficacy of the method and the effect of some hyperparameters.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n* The method is interesting and effective. First, the pseudo-labeling dynamic learning can make more efficient use of the current model since they predict the pseudo-labels using the up-to-date model. Second, the cyclic batch matching with a dynamic weight can stabilize the training process and decrease the bad effect of the incorrect pseudo-labels. The method is novel to me, and the effectiveness is verified by the experiments.\n\nCons:\n* The writing needs to be polished, especially the math notations. Those conflicting notations largely hinder a smooth reading for the readers and raise some misunderstandings. There are so many conflicting math notations. In Line 107, you use $k$ to denote the class number, while it also represents the batch index later in Line 169. In Line 113, the model is a mapping from $X$ to $Y=\\{1,2,...,k\\}$, while the expressions later in Eq. (2), (3) indicate it is a mapping from $X$ to $[0,1]^k$. In Line 127, you add the $sign$ to denote the final prediction function $\\hat{f}$ but this is only for binary classification---you use a multi-classification setting in Line 107. In Line 146, you use $D$ to denote a domain set. Since it does not have a subscript, I was wondering if this indicates a dataset combined from all domains or if it indicates one specific domain $D_t$? In Eq. (11), $x_i$ or $x$? In Line 176, the domain is denoted as $D_i$, while the index in the previous section is $t$ for the domain (Line 128). I totally understand that the gradual domain adaptation problem, which involves many domains, increases the complexity of the notation system, but those conflicting notations make it difficult for readers to understand the method of this work.\n* The motivations of the method should be explained more clearly. In section 4.2, the motivation of combining two batches from two adjacent domains is not clear. To my understanding, I think the idea is a little bit similar to Momentum. When the weight $\\rho$ is small at the early stage, the model is trained based on the loss on the previous domain rather than the current domain, where the accuracy of the pseudo-label could be high. So this strategy could prevent the model from collapsing due to the incorrect pseudo-labels of the current domain.\n* The framework illustration in Fig. 2 is not clear. What is the meaning of \"sync\" in the figure? To my understanding, you use only one classifier to learn the batches $B_t$ and $B_{t+1}$. I suggest replacing \"sync\" with \"shared weights,\" which is more widely used in papers. Besides, what is $H_t$? In the method, you never use the feature extractor and classifier in a separate way. So is it really necessary to separate the network into three parts—classifier, $H_t$, and feature extractor—in the illustration figure? \n* More ablation studies are needed. I suggest the authors validate the effects of the three components in Section 4 via an ablation study. For example, you could remove the cyclic batch matching and compare the results to show the important role of cyclic batch matching.\n* As the authors note in Line 141, the mislabeling would hurt performance. So it would be more convincing if the average accuracy of the pseudo-labels on intermediate domains can be shown and compared to further validate the effectiveness of your method.\n* Another baseline method. The work of [1] proposes an adversarial self-training method, which significantly improves the performance of GDA. Please consider comparing your method with [1].\n\n[1]: Adversarial Self-Training Improves Robustness and Generalization for Gradual Domain Adaptation. Lianghe Shi, Weiwei Liu. NeurIPS 2023.\n\nQUESTIONS:\n* In the GST method, a new model is trained from scratch on each domain, while this work trains only one model and fine-tunes the model in each iteration. I am a little curious about whether keeping fine-tuning only one model would also improve the performance of the GST method.\n\nLIMITATIONS:\nYes, the authors discuss some limitations of their method in the appendix.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 1, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-16"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces Self-Training with Dynamic Weighting (STDW) to Gradual Domain Adaptation (GDA). It addresses the challenge of smooth knowledge migration from source to target domains by introducing a dynamic weighting mechanism controlled by a time-varying hyper-parameter ϱ, which balances the loss contributions of source and target domains during training. The method leverages self-training to generate pseudo-labels and optimizes a weighted objective function for iterative model updates. The authors provide theoretical insights and validate the method through extensive experiments on multiple benchmark datasets, demonstrating that STDW outperforms existing baselines and achieves SOTA performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1)\tFormal analysis (optimal transport, Lyapunov stability) provides error bounds for pseudo-label consistency and convergence (Appx. B). \n2)\tThe paper introduces a novel dynamic weighting mechanism that adaptively balances the loss contributions of source and target domains during training. This innovation effectively addresses the challenges of GDA.\n3)\tExtensive validation across 6 datasets, varying intermediate domains (0–6), and 15 corruption types. Ablation studies confirm criticality of ϱ scheduling and intermediate domains.\n4)\tThe proposed STDW method has demonstrated significant improvements in handling continuous distribution shifts compared to existing methods. \n\nWeaknesses:\n1)\tThe performance of STDW relies heavily on the availability and quality of intermediate domains, which may not always be accessible or well-defined in real-world scenarios. This limitation could restrict the applicability of the method in certain practical situations.\n2)\tThe dynamic weighting mechanism introduces additional hyperparameters (e.g., the scheduling of ϱ) that require careful tuning, potentially increasing the computational overhead during training. \n3)\tThe method assumes a smooth transition between domains, which might not hold for highly discontinuous or abrupt domain shifts. The authors acknowledge this limitation and suggest that future work could explore ways to handle more diverse and challenging domain shifts.\n\nQUESTIONS:\n1)\tThe paper assumes a smooth transition between domains. How would the method perform in scenarios with highly discontinuous or abrupt domain shifts? Could the authors provide some insights or potential solutions for such cases?\n2)\tThe linear ϱ schedule (0→1) is heuristic. Are there theoretical or data-driven guidelines (e.g., based on domain discrepancy)? Could ϱ be learned?\n3)\tThe paper mentions that the performance of STDW relies on the availability and quality of intermediate domains. What strategies could be employed to generate or select high-quality intermediate domains in cases where they are not readily available?\n4)\tCyclic batch matching and iterative pseudo-labeling may increase training time. How does STDW’s complexity compare to baselines (e.g., GST)?\n\nLIMITATIONS:\nYes, The authors have adequately addressed the limitations of their work in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Self-Training with Dynamic Weighting (STDW), a novel method designed for Gradual Domain Adaptation (GDA) that improves robustness by smoothly migrating knowledge from the source domain to the target domain. STDW generates dynamic pseudo-labels and uses a time-dependent hyperparameter to progressively shift learning emphasis from source to target domains. Empirical results on various benchmarks (demonstrate that STDW significantly surpasses existing GDA approaches.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n- Paper is clearly written.\n- Performance is strong.\n- Setting is practical.\n\n**Weaknesses**\nOverall, I don't think the contribution from this paper is very novel.\n1. Generating dynamic pseudo-labels is already common in self-training [1]. This is very similar to the approach Section 4.1. FixMatch even has an additional step of selecting high-confidence pseudo-labels.\n2. The paper proposes cyclic batch matching in Section 4.2. But I am confused why data sampling is designed in such a manner. One can simply randomly sample a batch from the left and right domain each time. This simplifies the training pipeline, and I feel it wouldn't impact the performance too much.\n3. While the stepwise dynamic osmosis makes a lot of sense, it feels like an engineering technique (or hyperparameter tweaking) and offers little technical contribution.\n\n[1] FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence\n\nQUESTIONS:\n1. Does random sampling perform about the same with cyclic batch matching? If not, can you please offer an explanation why cyclic batch matching work?\n2. While I think gradual domain adaptation is a more practical assumption than traditional UDA, it can be difficult to collect data from gradual domains in practical situations. What is your view of this?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a novel method, Self-Training with Dynamic Weighting (STDW), for the problem of Gradual Domain Adaptation (GDA). This method employs a self-training mechanism with pseudo-labels and introduces a dynamic hyperparameter ϱ, which evolves from 0 to 1 to balance the contributions of the source and target domains in the loss function. This dynamic weighting ensures a smooth and controlled knowledge transfer across intermediate domains during adaptation. This mechanism further incorporates a dynamic, mini-batch-wise pseudo-label generation strategy to mitigate the accumulation of pseudo-labeling errors during continual adaptation. The proposed method is evaluated on multiple image datasets and consistently outperforms both traditional domain adaptation approaches and other state-of-the-art GDA methods. Comprehensive ablation studies further confirm the essential role of the dynamic parameter ϱ and the use of intermediate adaptation steps, which together enhance stability and reduce domain bias.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Introduces a novel optimization framework employing a time-varying hyperparameter ϱ, which flexibly adjusts the relative contributions of the source and target domains during adaptation, thereby ensuring a stable and effective knowledge transfer across domains.\n2. Instead of generating pseudo-labels for the entire dataset in a single pass, the model produces pseudo-labels on a mini-batch basis and updates itself iteratively. This dynamic approach mitigates the accumulation of pseudo-labeling errors during adaptation, thereby enhancing stability and robustness throughout the gradual adaptation process\n\nWeaknesses:\n1. The method has not been compared with the most recent state-of-the-art approaches.\n2. The proposed approach does not demonstrate a significant breakthrough compared to existing methods.\n3. The effectiveness of the method largely depends on the identification and availability of intermediate domains. In practice, intermediate data can sometimes be difficult to identify or incomplete, which limits its applicability.\n\nQUESTIONS:\nPlease refer to weaknesses.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}