Convolutional neural networks (CNNs) evolved from Fukushima's neocognitron model, which is based on the ideas of Hubel and Wiesel about the early stages of the visual cortex. Unlike other branches of neocognitron-based models, the typical CNN is based on end-to-end supervised learning by backpropagation and removes the focus from built-in invariance mechanisms, using pooling not as a way to tolerate small shifts but as a regularization tool that decreases model complexity. These properties of end-to-end supervision and flexibility of structure allow the typical CNN to become highly tuned to the training data, leading to extremely high accuracies on typical visual pattern recognition data sets. However, in this work, we hypothesize that there is a flip side to this capability, a hidden overfitting. More concretely, a supervised, backpropagation based CNN will outperform a neocognitron/map transformation cascade (MTC) when trained and tested inside the same data set. Yet if we take both models trained and test them on the same task but on another data set (without retraining), the overfitting appears. Other neocognitron descendants like the What-Where model go in a different direction. In these models, learning remains unsupervised, but more structure is added to capture invariance to typical changes. Knowing that, we further hypothesize that if we repeat the same experiments with this model, the lack of supervision may make it worse than the typical CNN inside the same data set, but the added structure will make it generalize even better to another one. To put our hypothesis to the test, we choose the simple task of handwritten digit classification and take two well-known data sets of it: MNIST and ETL-1. To try to make the two data sets as similar as possible, we experiment with several types of preprocessing. However, regardless of the type in question, the results align exactly with expectation.

Willshaw networks are single-layered neural networks that store associations between binary vectors. Using only binary weights, these networks can be implemented efficiently to store large numbers of patterns and allow for fault-tolerant recovery of those patterns from noisy cues. However, this is only the case when the involved codes are sparse and randomly generated. In this letter, we use a recently proposed approach that maps visual patterns into informative binary features. By doing so, we manage to transform MNIST handwritten digits into well-distributed codes that we then store in a Willshaw network in autoassociation. We perform experiments with both noisy and noiseless cues and verify a tenuous impact on the recovered pattern's relevant information. More specifically, we were able to perform retrieval after filling the memory to several factors of its number of units while preserving the information of the class to which the pattern belongs.