  Inductive transfer learning aims to learn from a small amount of training
data for the target task by utilizing a pre-trained model from the source task.
Most strategies that involve large-scale deep learning models adopt
initialization with the pre-trained model and fine-tuning for the target task.
However, when using over-parameterized models, we can often prune the model
without sacrificing the accuracy of the source task. This motivates us to adopt
model pruning for transfer learning with deep learning models. In this paper,
we propose PAC-Net, a simple yet effective approach for transfer learning based
on pruning. PAC-Net consists of three steps: Prune, Allocate, and Calibrate
(PAC). The main idea behind these steps is to identify essential weights for
the source task, fine-tune on the source task by updating the essential
weights, and then calibrate on the target task by updating the remaining
redundant weights. Under the various and extensive set of inductive transfer
learning experiments, we show that our method achieves state-of-the-art
performance by a large margin.
