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一步一步的分布对齐的风格快速调整为源代码免费的跨域短拍学习
IEEE transactions on pattern analysis and machine intelligence
|September 16, 2025
概括
本研究为没有源数据的大型模型引入了无源跨域短拍学习 (SF-CDFSL). 拟议的逐步分布调整式样式提示调整 (StepSPT) 方法隐式地减少了域差距,以提高少数拍摄的学习性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 跨领域的少量学习 (CDFSL) 方法与大型预训练模型 (LMs) 斗争,原因是无法访问源数据和培训策略.
- 对CDFSL进行微调LM的计算成本昂贵,限制了实际应用.
研究的目的:
- 调查无源CDFSL (SF-CDFSL) 问题,使目标领域的少数射击学习 (FSL) 只使用预训练模型和有限的目标样本.
- 解决隐式缩小领域差距的挑战,而无需访问源数据.
主要方法:
- 提出逐步分布对齐的风格提示调 (StepSPT),这是SF-CDFSL的一种新方法.
- 使用风格提示符来调整目标样本以达到预期的分布.
- 采用双相优化流程:外部流程用于逐步调整样式提示符的分布,内部流程用于分类器更新.
主要成果:
- 在五个数据集上,StepSPT展示了对现有的提示调整方法和最先进的方法的优越性.
- 废弃研究证实了拟议的StepSPT方法的有效性.
- 绩效分析突出了分销优化战略的有效性.
结论:
- StepSPT为SF-CDFSL提供了实用和有效的解决方案,特别是对于大型预训练模型.
- 该方法通过优化预测分布来隐性地减少域间隙,克服无源场景的局限性.
- 步骤SPT通过使LMs能够有效地适应新领域,从而推进了少量学习领域.
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