  The forward-forward algorithm presents a new method of training neural
networks by updating weights during an inference, performing parameter updates
for each layer individually. This immediately reduces memory requirements
during training and may lead to many more benefits, like seamless online
training. This method relies on a loss ("goodness") function that can be
evaluated on the activations of each layer, of which can have a varied
parameter size, depending on the hyperparamaterization of the network. In the
seminal paper, a goodness function was proposed to fill this need; however, if
placed in a one-class problem context, one need not pioneer a new loss because
these functions can innately handle dynamic network sizes. In this paper, we
investigate the performance of deep one-class objective functions when trained
in a forward-forward fashion. The code is available at
\url{https://github.com/MichaelHopwood/ForwardForwardOneclass}.
