We introduce a novel method that enables parameter-efficient transfer and
multi-task learning with deep neural networks. The basic approach is to learn a
model patch - a small set of parameters - that will specialize to each task,
instead of fine-tuning the last layer or the entire network. For instance, we
show that learning a set of scales and biases is sufficient to convert a
pretrained network to perform well on qualitatively different problems (e.g.
converting a Single Shot MultiBox Detection (SSD) model into a 1000-class image
classification model while reusing 98% of parameters of the SSD feature
extractor). Similarly, we show that re-learning existing low-parameter layers
(such as depth-wise convolutions) while keeping the rest of the network frozen
also improves transfer-learning accuracy significantly. Our approach allows
both simultaneous (multi-task) as well as sequential transfer learning. In
several multi-task learning problems, despite using much fewer parameters than
traditional logits-only fine-tuning, we match single-task performance.

本文介绍了一种基于小参数集合的深度神经网络迁移学习和多任务学习的新方法，通过调整网络中的特定组件使其适应不同任务，以达到在多个任务中共享参数并且性能表现良好的目的。