An Information-Theoretic Metric of Transferability for Task Transfer Learning

By Yajie Bao, Yongni Li, Shao-Lun Huang, Lin Zhang, Amir Zamir, L. Guibas. 2018

Subfields: Representation & Self-Supervised Learning, Information Theory

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Representation & Self-Supervised Learning

1. Hayden S. Helm
2. Carey E. Priebe
3. Brandon Duderstadt
4. Ronak D. Mehta
5. Ashwin De Silva
6. Carey E. Priebe
7. Joshua T. Vogelstein
8. Ali Geisa

In Information Theory

1. Carey E. Priebe

Citations in the corpus

• A partition-based similarity for classification distributions (2020, background): “4 RELATED WORK There has been a flurry of recent empirical work (Kifer et al., 2004; Achille et al., 2019; Bao et al., 2018; Tran et al., 2019; Bhattacharjee et al., 2020; Nguyen et al., 2020) defining statistics that help to determine whether or not a particular model or representation learned on a”
• Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis (2023, background): “Measures of task similarity The similarity between two tasks can be measured in various ways and is typically used to determine how fit a pre-trained model is for a particular target task (Bao et al., 2018; Tran et al., 2019; Nguyen et al., 2020) or to define a taxonomy of tasks (Zamir et al.”