What is the term for when knowledge is transferred from a less related source and may degrade target performance?

The correct term for when knowledge is transferred from a less related source that may ultimately degrade the performance of the target model is known as Negative Transfer Learning. This phenomenon occurs in scenarios where the source task is not sufficiently similar to the target task, leading to the transfer of irrelevant knowledge that can confuse the learning process of the target model. Consequently, instead of enhancing performance, the transferred knowledge can lead to an increase in error rates or ineffectiveness in the specific context of the target task.

When considering the other options, Positive Transfer Learning refers to successful knowledge transfer that improves performance on the target task, making it distinctly different from the context of negative effects. Adaptive Transfer Learning implies a process where the model adjusts or fine-tunes itself based on new data; this doesn't necessarily relate to negative impacts. Unsupervised Learning is a paradigm of machine learning that deals with data without labeled responses and doesn't fit the concept of transferring knowledge between tasks. Each of these focuses on different aspects of machine learning, further highlighting the appropriateness of Negative Transfer Learning for the situation described.