Overfitting in deep learning models occurs when a model learns to perform very well on the training data but fails to generalize to unseen data. In simple terms, it means that the model has memorized the training set rather than learning the underlying patterns that apply more broadly. This typically happens when a model is too complex relative to the amount of data available. For example, if you have a neural network with many layers and parameters, it might capture noise in the training data instead of just the signal that reflects the true relationships.
A common scenario that leads to overfitting is having a small dataset with a very powerful model. For instance, if you are trying to classify images of cats and dogs but only have 100 images for each class, a deep neural network can easily learn to identify specific features in those images while ignoring general characteristics that would apply to new, unseen images. This can manifest as high accuracy on the training set but poor performance when the model is tested on a validation or test set due to its inability to adapt to variations in new data.
To combat overfitting, developers can use several techniques. One approach is to simplify the model, either by reducing the number of layers and parameters or by using techniques such as dropout, which randomly "drops" units during training to prevent them from co-adapting too closely to the training data. Data augmentation can also be beneficial, where the training data is artificially expanded through transformations like rotation or scaling, thereby exposing the model to more variations. Finally, employing early stopping—where training is halted once performance on a validation set starts to degrade—can help ensure that the model maintains its capability to generalize to new inputs.