Overcoming Negative Transfer: A Survey

By Wen Zhang, Lingfei Deng, Dongrui Wu. 2020

53 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. Ying Wei
2. Qiang Yang
3. Junzhou Huang
4. Long-Kai Huang
5. Yu Rong
6. Rui Yang
7. Yihao Xue
8. Mengjie Huang

Citations in the corpus

• AFEC: Active Forgetting of Negative Transfer in Continual Learning (2021, neutral): “…transfer is negative , i.e., learning a new task from the old knowledge is worse than learning the new task on a randomly-initialized network [37, 17], then precisely remembering the old tasks will severely interfere with the learning of the new task, thus decreasing the performance of…”
• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, background): “If a priori knowledge is transferred from an unrelated field, the performance of transfer learning can be greatly affected, as not all the knowledge extracted from the source domain effectively improves the performance of the target task [167, 168].”
• Frustratingly Easy Transferability Estimation (2021, background): “could vary a lot, even with the possibility of negative transfer (Pan & Yang, 2009; Wang et al., 2019; Zhang et al., 2020).”