Graphs have been widely adopted to denote structural connections between entities. The
relations are in many cases heterogeneous, but entangled together and denoted merely as …

Abstract We introduce Task Switching Networks (TSNs), a task-conditioned architecture with
a single unified encoder/decoder for efficient multi-task learning. Multiple tasks are …

Transferability estimation has been an essential tool in selecting a pre-trained model and
the layers in it for transfer learning, to transfer, so as to maximize the performance on a target …

In recent years, remarkable achievements have been made in artificial intelligence tasks
and applications based on deep neural networks (DNNs), especially in the fields of vision …

Abstract Figuring out which Pre-Trained Model (PTM) from a model zoo fits the target task is
essential to take advantage of plentiful model resources. With the availability of numerous …

Deep transfer learning has become increasingly prevalent in various fields such as industry
and medical science in recent years. To ensure the successful implementation of target …

Abstract Knowledge distillation has demonstrated encouraging performances in deep model
compression. Most existing approaches, however, require massive labeled data to …

The last decade has witnessed the success of deep learning and the surge of publicly
released trained models, which necessitates the quantification of the model functional …

We address the problem of ensemble selection in transfer learning: Given a large pool of
source models we want to select an ensemble of models which, after fine-tuning on the …

This paper is concerned with ranking many pre-trained deep neural networks (DNNs), called
checkpoints, for the transfer learning to a downstream task. Thanks to the broad use of …