K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning

Understand

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).