Description: Sparsity-Inducing Norms are regularization techniques used in deep learning that aim to limit the complexity of models during the training process. These norms act as constraints that promote sparsity in the model’s parameters, meaning that they seek to prevent the model from fitting too closely to the training data, a phenomenon known as overfitting. By inducing sparsity, the generalization of the model is promoted, allowing it to perform better on unseen data. The most common norms include L1 and L2, which penalize the magnitude of the model’s coefficients. The L1 norm, also known as Lasso regularization, tends to produce sparser models by completely eliminating some parameters, while the L2 norm, or Ridge regularization, distributes the penalty more evenly among all parameters. These techniques are fundamental in designing robust and efficient models, as they help balance model complexity and the amount of available data, which is crucial in applications where data is limited or noisy. In summary, Sparsity-Inducing Norms are essential tools in deep learning that enable the construction of more general models that are less prone to prediction errors.
