  The recent successes in analyzing images with deep neural networks are almost
exclusively achieved with Convolutional Neural Networks (CNNs). The training of
these CNNs, and in fact of all deep neural network architectures, uses the
backpropagation algorithm where the output of the network is compared with the
desired result and the difference is then used to tune the weights of the
network towards the desired outcome. In a 2022 preprint, Geoffrey Hinton
suggested an alternative way of training which passes the desired results
together with the images at the input of the network. This so called Forward
Forward (FF) algorithm has up to now only been used in fully connected
networks. In this paper, we show how the FF paradigm can be extended to CNNs.
Our FF-trained CNN, featuring a novel spatially-extended labeling technique,
achieves a classification accuracy of 99.16% on the MNIST hand-written digits
dataset. We show how different hyperparameters affect the performance of the
proposed algorithm and compare the results with CNN trained with the standard
backpropagation approach. Furthermore, we use Class Activation Maps to
investigate which type of features are learnt by the FF algorithm.
