MoE-Adapters++:通过动态混合专家适配器实现视觉语言模型的更有效的持续学习
IEEE transactions on pattern analysis and machine intelligence
|August 11, 2025
概括
本研究介绍了MoE-Adapters和MoE-Adapters++以改善视觉语言模型 (VLM) 的持续学习. 这些方法有效地将预先训练的模型适应新任务,同时保持性能和降低复杂性.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 视觉语言模型 (VLM) 中的增量学习面临长期遗忘的挑战.
- 现有的方法很难有效地将像CLIP这样的预训练模型适应新的任务,而不会降低性能.
研究的目的:
- 提出MoE-Adapters,一个参数效率高的框架,以减轻VLM增量学习中的遗忘.
- 开发MoE-Adapters++,一个更加统一和高效的架构,用于VLM的持续学习.
主要方法:
- MoE-Adapters使用逐步增加的路由器和静态专家适配器来有效地适应任务.
- 分布区分自动选择器 (DDAS) 路由输入以保持零射击能力.
- MoE-Adapters++引入了动态适配器和一个潜在嵌入式自动选择器 (LEAS) 来实现统一的架构.
主要成果:
- 提出的方法在不同的增量学习环境中始终优于最先进的方法.
- MoE-Adapters++显示了提高训练效率和减少参数冗余.
- 这些框架有效地保护了预先训练有素的VLMs的零射击能力.
结论:
- MoE-Adapters和MoE-Adapters++提供了有效的解决方案,用于VLM中的参数有效的持续学习.
- 动态适配器和统一选择器方法提高了适应性和效率.
- 这些进步有助于更强大的和可扩展的VLM开发.
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