本文提出了一种名为条件适配器（CoDA）的参数有效的迁移学习方法，同时也提高了推理效率。CoDA不仅是标准适配器方法的一种推广，还通过条件计算实现了一种平衡速度和准确性的新方法。从一个现有的经过密集预训练的模型开始，CoDA添加了稀疏激活，再加上少量的新参数和轻量级训练阶段。本文的实验表明，CoDA方法提供了一种出人意料的有效的知识迁移方式。在各种语言、视觉和语音任务中，与最先进的适配器方法相比，CoDA实现了2倍到8倍的推理速度提升，且中等或无精度损失，并保持了相同的参数效率。
总结：
提出了一种名为“Conditional Adapters”的新型参数化轻量级网络结构。这种结构可以在迁移学习过程中有效地共享特征，从而大幅提高模型的可再用性和节约时间。在这种结构中，通过添加一些额外的适配器层，可以使旧模型更好地适应新的任务，同样也可以避免在没有充足数据的情况下进行训练，从而大大提高了训练效率。最终，这种新型网络结构可以用来完成图像分类和目标检测等各种任务。
标题：Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference
作者：Tao Lei, Junwen Bai, Siddhartha Brahma, Joshua Ainslie, Kenton Lee, Yanqi Zhou, Nan Du, Vincent Y. Zhao, Yuexin Wu, Bo Li, Yu Zhang, Ming-Wei Chang
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