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Life sciences · Preprint
arXiv · August 11, 2026
Posted before peer review. The findings may change or fail to hold.
BPG is a proposed machine learning framework for domain incremental learning that uses adaptive parameter allocation and soft domain mixture at inference to improve generalization and reduce catastrophic forgetting. The work is a preprint validation on standard computer vision benchmarks with no peer review, clinical translation, or human outcomes.
Preprint. Standard computer vision benchmark datasets. Intervention: BPG framework with BPG-Adapter (dynamic adapter sizing) and BPG-Inference (soft domain mixture). Compared with: Uniform adapter-based approaches and hard domain selection strategies.
BPG achieves state-of-the-art average accuracy on DomainNet, CDDB, and CORe50 benchmarks Forgetting reduced to as low as 0.22% on DomainNet Outperforms uniform adapter-based approaches and hard domain selection strategies
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This is an unrefereed arXiv preprint presenting a machine learning method with experimental validation on benchmark datasets, but lacking peer review and clinical or translational relevance.
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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.
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