Forward Pas

Forward pass-only methods in neural network training aim to improve efficiency and biological plausibility by eliminating the computationally expensive backward pass used in backpropagation. Current research focuses on developing alternative algorithms like the Forward-Forward algorithm and its variants, exploring their application in various tasks including continual learning, large language model inference, and real-time video processing. These efforts are significant because they could lead to faster training times, reduced resource consumption, and potentially more biologically realistic models, impacting both the efficiency of AI systems and our understanding of neural computation.