用零样本专家模型组合实现持续学习，效率更高
Task-Agnostic Experts Composition for Continual Learning
- 通过组合零样本专家模型实现任务无关的持续学习
- 在挑战性基准上准确率显著优于基线方法
- 计算开销更小，适合资源受限场景
组合性是人类推理的核心能力之一，能将复杂问题分解为简单单元。这一特性对神经网络同样关键，尤其在构建更高效、可持续的AI系统时。我们提出一种基于零样本专家模型集成的组合方法，并在专门设计用于测试组合能力的挑战性基准上验证该方法。实验表明，我们的专家组合方法在保持更低计算资源消耗的同时，显著提升了准确率，展现出更高的效率。
原文摘要 · Abstract (English)
Compositionality is one of the fundamental abilities of the human reasoning process, that allows to decompose a complex problem into simpler elements. Such property is crucial also for neural networks, especially when aiming for a more efficient and sustainable AI framework. We propose a compositional approach by ensembling zero-shot a set of expert models, assessing our methodology using a challenging benchmark, designed to test compositionality capabilities. We show that our Expert Composition method is able to achieve a much higher accuracy than baseline algorithms while requiring less computational resources, hence being more efficient.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。