Introducing LiNeS, a new technique that prevents forgetting and enhances model merging in neural networks! This innovative approach addresses the issue of catastrophic forgetting, where models forget information from earlier tasks as they learn new ones. By scaling the weights of individual layers after training, LiNeS allows models to retain knowledge from previous tasks while adapting to new data.
This breakthrough has significant implications for applications like federated learning and lifelong learning, where combining and reusing knowledge is crucial. The technique outperforms existing methods like joint training and fine-tuning, making it a promising solution for tackling the challenges of continual learning.