When training deep neural networks, overfitting is a major problem. Your model may perform exceptionally well on training data but struggle on unseen test data. Dropout regularization is one technique used to tackle overfitting problems in deep learning. Dropout help prevent networks from memorizing the training data, forcing them to learn meaningful patterns.
How does Dropout work?
During training, dropout randomly disables (sets to zero) a fraction of neurons in each layer. This forces the network to learn more robust, distributed representations, making it more resilient and reducing overfitting.
✅ Prevents overfitting
✅ Improves generalization
During inference (the model's evaluation phase), dropout is turned off, and all neurons are utilized to make predictions.
Visual Comparison
I’m presenting a visual comparison on CIFAR data with and without dropout to illustrate its impact. In the No_Dropout_Model (Without Dropout), overfitting begins around the 5th epoch, the model continues to improve on the training data, but its performance on validation data does not improve. You can also notice the training and validation loss start to diverge.
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