Early dropout is a technique used in deep learning to prevent the problem of underfitting neural networks. Introduced in 2012, dropout has become a popular method to avoid overfitting. However, dropout can also be used in the early stages of training to help mitigate underfitting. The technique involves adding dropout only during the initial phase of model training and turning it off afterward.
What is dropout?
Dropout is a regularization technique that helps prevent the problem of overfitting in neural networks. Overfitting occurs when a model learns to fit the training data too well and does not perform well on unseen data. Dropout works by randomly dropping out a fraction of nodes in the neural network during training. This randomness helps the network to generalize better and prevents overfitting.
The problem of underfitting
Underfitting is another problem that can occur in neural networks. It happens when the model is not complex enough to fit the training data. In other words, the model does not have enough capacity to learn the underlying patterns in the data. This can lead to poor performance on both training and testing data.
The traditional solution for underfitting is to increase the model's capacity by adding more layers or neurons. However, this approach can lead to overfitting if the model is too complex. Early dropout is a solution that can help mitigate underfitting without increasing the model's capacity.
How early dropout works
Early dropout involves adding dropout only during the initial phases of training. This helps to reduce the directional variance of gradients across mini-batches and align the mini-batch gradients with the entire dataset's gradient. This, in turn, helps to counteract the stochasticity of SGD (Stochastic Gradient Descent) and limit the influence of individual batches on model training.
The idea behind early dropout is to add some randomness to the model during the early stages of training when the model is learning the underlying patterns in the data. This helps the model to generalize better and prevents underfitting.
Late dropout
Late dropout is a symmetric technique to regularize overfitting models. In late dropout, dropout is not used in the early iterations and is only activated later in training. This helps to prevent the model from overfitting the training data. Late dropout is typically used when the model's capacity is high, and overfitting is a concern.
Experimental results
Experiments on ImageNet and various vision tasks demonstrate that early dropout consistently improves generalization accuracy. Models equipped with early dropout achieve lower final training loss compared to their counterparts without dropout. This highlights the usefulness of early dropout as a tool for improving model performance during the early stages of training.
Early dropout is a powerful technique for preventing underfitting in deep learning models. By adding dropout only during the initial phases of training, the technique helps to counteract the stochasticity of SGD and limit the influence of individual batches on model training. This leads to improved performance in underfitting models. Late dropout is another regularization technique that can be used for overfitting models. Experimental results have shown that both techniques can consistently improve generalization accuracy across various vision tasks.
