What is Overfitting in Machine Learning?
Overfitting is a common challenge in machine learning, particularly in the context of computer vision. It occurs when a model learns not only the underlying patterns in the training data but also the noise and fluctuations. This results in a model that performs exceptionally well on the training set but poorly on unseen data or validation sets.
In computer vision, overfitting can manifest when a model captures specific features that are unique to the training images rather than generalizing from them. For instance, if a neural network is trained on a small dataset of cat images, it might memorize specific breeds or backgrounds instead of identifying general characteristics of cats. Consequently, when presented with new images, the model may fail to recognize various types of cats.
To mitigate overfitting, several strategies can be employed. Regularization techniques such as L1 and L2 regularization help penalize complexity in the model. Additionally, data augmentation techniques, which artificially expand the training dataset by applying transformations, can improve generalization. Techniques like dropout during training can also help in creating more robust models that perform well on unseen data.
Understanding and addressing overfitting is essential in the field of artificial intelligence. By ensuring that models generalize well, we enhance their practical applicability and reliability across diverse datasets and scenarios.