  Recent deep learning models such as ChatGPT utilizing the back-propagation
algorithm have exhibited remarkable performance. However, the disparity between
the biological brain processes and the back-propagation algorithm has been
noted. The Forward-Forward algorithm, which trains deep learning models solely
through the forward pass, has emerged to address this. Although the
Forward-Forward algorithm cannot replace back-propagation due to limitations
such as having to use special input and loss functions, it has the potential to
be useful in special situations where back-propagation is difficult to use. To
work around this limitation and verify usability, we propose an Unsupervised
Forward-Forward algorithm. Using an unsupervised learning model enables
training with usual loss functions and inputs without restriction. Through this
approach, we lead to stable learning and enable versatile utilization across
various datasets and tasks. From a usability perspective, given the
characteristics of the Forward-Forward algorithm and the advantages of the
proposed method, we anticipate its practical application even in scenarios such
as federated learning, where deep learning layers need to be trained separately
in physically distributed environments.
