[
    {
        "id": "osp-21531",
        "type": "article-journal",
        "title": "Decoupled and Distilled: Task-Adaptive LoRA-Teachers with Ensemble Knowledge Transfer for Few-Shot Class-Incremental Learning",
        "author": [
            {
                "family": "Zhao",
                "given": "Hongwei"
            },
            {
                "family": "Liu",
                "given": "Rui"
            },
            {
                "family": "Liu",
                "given": "Yansong"
            },
            {
                "family": "Zou",
                "given": "Zhiyuan"
            },
            {
                "family": "Chen",
                "given": "Yong"
            }
        ],
        "URL": "https://omanscience.com/en/articles/decoupled-and-distilled-task-adaptive-lora-teachers-with-ensemble-knowledge-transfer-for-few-shot-class-incremental-learning",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "DOI": "10.1016/j.neucom.2026.135200",
        "abstract": "Few-Shot Class-Incremental Learning (FSCIL) addresses the challenge of learning new classes from very limited samples while retaining knowledge of previously learned ones. Although parameter-efficient fine-tuning methods with pre-trained models show promise for class-incremental learning, strict gradient-based constraints can be unreliable under severe data scarcity, while multi-expert approaches can impose substantial inference-time costs. We propose TALON (Task-Adaptive LoRA-Teachers with Ensemble Knowledge Transfer), an inference-efficient FSCIL framework. TALON dynamically allocates an independent LoRA-Teacher to each incremental task for task-specific representation learning, then distills multiple frozen teachers into a unified LoRA-Student through Ensemble Knowledge Transfer, eliminating runtime module selection or generation. A semantic-guided distillation strategy weights teacher contributions by feature-space similarity to mitigate catastrophic forgetting and overfitting. Across three class-order runs, TALON achieves comparable or better mean average accuracy across four FSCIL benchmarks, obtaining 86.68 +/- 1.22% on CUB200, 90.39 +/- 0.27% on CIFAR100, 78.38 +/- 0.94% on ImageNet-R, and 96.34 +/- 0.33% on miniImageNet. TALON uses up to 33x fewer deployment parameters and reduces average inference time per task to 26.7 s, a 41.70% reduction relative to ASP."
    }
]