Overfitting and Regularization in CNN

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Overfitting and Regularization in CNN

In this article, I am going to discuss Overfitting and Regularization in CNN. Please read our previous article where we discussed ConvNet Architecture in CNN.

Overfitting and Regularization in CNN

Overfitting is a typical issue in machine learning in which a model performs well on training data but badly on unknown data. It occurs when the model is too complicated and has too many parameters in comparison to the quantity of training data, causing it to learn patterns particular to the training data rather than universal patterns that will generalize to unknown data.

Regularization is a strategy for combating overfitting that involves adding a penalty to the objective function of the model to prevent it from learning patterns that are overly unique to the training data. The objective is to strike a compromise between a model that is complex enough to fit the training data effectively and one that is not so complicated that it begins to learn patterns that are particular to the training data and do not generalize to unobserved data.

Regularization can take numerous forms, including:

L1 Regularization imposes a penalty on the objective function that is proportional to the absolute value of the model’s parameters. As a result, the model is sparse, with some of its parameters being exactly zero.

L2 Regularization involves applying a penalty to the objective function that is proportional to the square of the model’s parameters. As a result, the parameters in the model are small, but not necessarily zero.

Early Stopping: Early stopping is a type of regularization that involves training the model until the performance on the validation set starts to degrade, and then stopping the training process. This helps to prevent the model from overfitting to the training data.

Dropout: Dropout is a regularization technique that involves randomly setting a fraction of the model’s neurons to zero during training. This helps to prevent the model from relying too heavily on any one neuron, which can help to reduce overfitting.

These are only a few instances of overfitting-fighting regularization approaches. The appropriate regularization strategy will be determined by the nature of the problem and the model being employed.

In the next article, I am going to discuss Max Pooling and ReLU Activations in CNN. Here, in this article, I try to explain Overfitting and Regularization in CNN. I hope you enjoy this Overfitting and Regularization in CNN article. Please post your feedback, suggestions, and questions about this Overfitting and Regularization article.