Transfer Learning via Learning to Transfer

By Ying Wei, Yu Zhang, Junzhou Huang, Qiang Yang. 2018

167 citations recorded by the source corpus.

How researchers see this paper

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

1. Han-Jia Ye
2. Qiang Yang
3. De-chuan Zhan
4. Yu Zhang
5. Wei-Lun Chao
6. Jun Shu
7. Da-Wei Zhou
8. Tal Hassner

Citations in the corpus

• Meta-Transfer Learning for Few-Shot Learning (2018, background): “Transfer learning What and how to transfer are key issues to be addressed in transfer learning, as different methods are applied to different source-target domains and bridge different transfer knowledge [34, 56, 54, 58].”
• Learning to Multitask (2018, background): “There are some learning paradigms related to the L2MT framework, including multitask learning, transfer learning [27], lifelong learning [12], and learning to transfer [33].”
• OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations (2021, background): “[39] propose to learn transfer skills from previous transfer learning experiences, and then apply such skills for future target tasks.”
• LEEP: A New Measure to Evaluate Transferability of Learned Representations (2020, methodology): “…research areas in machine learning and computer vision, including transfer learning (Weiss et al., 2016; Yang et al., 2020), meta-transfer learning (Wei et al., 2018; Sun et al., 2019; Requeima et al., 2019), task space modeling (Zamir et al., 2018; Achille et al., 2019), and domain adaptation…”
• A Decade Survey of Transfer Learning (2010–2020) (2020, background): “Recently, a learning to transfer (L2T) framework [115] introduced a way to self-select an algorithm based on the input data.”
• Transferability and Hardness of Supervised Classification Tasks (2019, methodology): “Our work is related to many fields in machine learning and computer vision, including transfer learning [67], meta learning [54], domain shifting [52], and multi-task learning [28].”
• Revisiting Meta-Learning as Supervised Learning (2020, background): “, 2017), transfer learning (Balaji et al., 2018; Ying et al., 2018), unsupervised learning (Metz et al.”
• How to Train Your MAML to Excel in Few-Shot Classification (2021, background): “Recent years have witnessed an abundance of new approaches on meta-learning [29, 30, 48, 61, 63, 64, 68, 77, 79, 80, 94, 98], developed in various areas including few-shot learning [70, 85, 89, 105, 107, 110, 111, 115], optimization [3, 9, 53, 108], reinforcement and imitation learning [23, 24, 31, ”
• Small Sample Learning in Big Data Era (2018, background): “The latest work was investigated by (Ying et al., 2018).”