A Survey on Negative Transfer

Transfer learning (TL) utilizes data or knowledge from one or more source domains to facilitate learning in a target domain. It is particularly useful when the target domain has very few or no labeled data, due to annotation expense, privacy concerns, etc. Unfortunately, the effectiveness of TL is not always guaranteed. Negative transfer (NT), i.e., leveraging source domain data/knowledge undesirably reduces learning performance in the target domain, and has been a long-standing and challenging problem in TL. Various approaches have been proposed in the literature to address this issue. However, there does not exist a systematic survey. This paper fills this gap, by first introducing the definition of NT and…

• NK
  National Key Research and Development Program of China

  Award: 2021ZD0201300