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标题: "BPG: Balancing Plasticity and Generalization for Domain Incremental Learning"
原文链接: "https://arxiv.org/abs/2608.10804"
发布日期: "2026-08-11"
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## Computer Science > Computer Vision and Pattern Recognition

## Title:BPG: Balancing Plasticity and Generalization for Domain Incremental Learning

Authors:[Qiang Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+Q), [Songlin Dong](https://arxiv.org/search/cs?searchtype=author&query=Dong,+S), [Shaokun Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+S), [Jizhou Han](https://arxiv.org/search/cs?searchtype=author&query=Han,+J), [Xiang Song](https://arxiv.org/search/cs?searchtype=author&query=Song,+X), [Chenhao Ding](https://arxiv.org/search/cs?searchtype=author&query=Ding,+C), [Yuhang He](https://arxiv.org/search/cs?searchtype=author&query=He,+Y), [Yihong Gong](https://arxiv.org/search/cs?searchtype=author&query=Gong,+Y)

[View PDF](https://arxiv.org/pdf/2608.10804) [HTML (experimental)](https://arxiv.org/html/2608.10804v1)

> Abstract:Deep neural networks excel in various tasks but struggle to generalize across evolving data distributions, leading to significant performance degradation under domain shifts. Domain incremental learning (DIL) addresses this challenge by enabling models to continuously adapt while retaining prior knowledge. Among existing DIL approaches, the parameter-isolation paradigm achieves state-of-the-art performance. However, these methods often adopt a one-size-fits-all approach to adapt to new domains, resulting in either insufficient learning capacity or redundant parameters. In this work, we propose BPG, a unified framework that addresses both challenges through two complementary components: BPG-Adapter, which dynamically determines each domain's adapter hidden dimension based on domain-specific feature separability, and BPG-Inference, a soft domain mixture strategy that integrates multiple domain-specific models at test time, mitigating domain ID misselection. Experimental results on DomainNet, CDDB, and CORe50 demonstrate that BPG consistently outperforms uniform adapter-based approaches and hard domain selection strategies, achieving state-of-the-art average accuracy while reducing forgetting to as low as 0.22% on DomainNet.

| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| --- | --- |
| Cite as: | [arXiv:2608.10804](https://arxiv.org/abs/2608.10804) \[cs.CV\] |
|  | (or [arXiv:2608.10804v1](https://arxiv.org/abs/2608.10804v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10804](https://doi.org/10.48550/arXiv.2608.10804) |

## Submission history

From: Qiang Wang \[[view email](https://arxiv.org/show-email/c754ccdb/2608.10804)\]  
**\[v1\]** Tue, 11 Aug 2026 11:22:27 UTC (2,996 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10804) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
