Fig. 1: Comparative diagram illustrating three distinct unsupervised (self-supervised) learning paradigms.
a Generation of a negative example is implemented by hybridization of two different images in the original FF paper27. b In Forward Forward (FF) Learning, the layer-wise loss function is defined so as to maximize the goodness for positive inputs (real images) and minimize the goodness for negative inputs, each of which is generated by corrupting the real image to form a fake image, as shown in (a). c In Contrastive Learning, two encoders independently process input images. The model is trained to maximize agreement between the latent representations of zi and zj, which are obtained from two augmented views of the same image, and to minimize agreement between the representations zi and zk, which are derived from different images. d Our proposed Contrastive Forward Forward Learning algorithm combines the principles of Forward Forward Learning and Contrastive Learning algorithms to maximize the goodness for concatenated similar pairs and minimize the goodness for dissimilar pairs with a layer-wise loss function.
