MLP memorization refers to a phenomenon in which a Multilayer Perceptron (MLP) neural network excessively memorizes the training data instead of generalizing and learning meaningful patterns. This can lead to poor performance on unseen or test data, resulting in overfitting.
During the training process, an MLP aims to find the optimal set of weights that minimizes the training error. However, if the network is too large or has excessive capacity relative to the complexity of the task, it can memorize the training examples individually, including their noise and idiosyncrasies, rather than learning the underlying patterns.
As a result, the network becomes overly specialized to the training data, unable to generalize well to new instances. When presented with unseen data, it may perform poorly due to its narrow focus on the specific training examples.
To address MLP memorization, various techniques can be employed, such as regularization methods (e.g., L1 or L2 regularization), early stopping, dropout, or reducing the network's capacity by adjusting the number of layers or neurons. These approaches help prevent overfitting and encourage the network to learn more generalized representations, enhancing its ability to make accurate predictions on new data.