  Transfer learning (TL) utilizes data or knowledge from one or more source
domains to facilitate the 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 the learning performance in the
target domain, has been a long-standing and challenging problem in TL. Various
approaches have been proposed in the literature to handle it. However, there
does not exist a systematic survey on the formulation of NT, the factors
leading to NT, and the algorithms that mitigate NT. This paper fills this gap,
by first introducing the definition of NT and its factors, then reviewing about
fifty representative approaches for overcoming NT, according to four
categories: secure transfer, domain similarity estimation, distant transfer,
and NT mitigation. NT in related fields, e.g., multi-task learning, lifelong
learning, and adversarial attacks, are also discussed.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning