Negative Transfer

Negative transfer, the detrimental effect of prior knowledge on learning new tasks, is a significant challenge in machine learning hindering the effectiveness of transfer learning. Current research focuses on mitigating negative transfer through various strategies, including selective sample weighting, task-aware model architectures (like those employing concept-wise fine-tuning or feature decomposition), and algorithmic modifications to existing methods such as multi-task learning and continual reinforcement learning. Overcoming negative transfer is crucial for improving the efficiency and robustness of machine learning systems across diverse applications, from fault diagnosis and recommendation systems to natural language processing and generative models.