What is Variational dropout

Variational Dropout: A Potent Regularization Technique for Deep Learning

As deep learning models continue to get more sophisticated, overfitting remains a critical challenge that limits their performance and generalization capabilities. Therefore, novel techniques are constantly being developed to mitigate overfitting and improve model robustness.

One such technique is dropout, which was introduced in 2012 by Srivastava et al. Dropout is a powerful regularization technique that helps reduce overfitting by randomly ignoring, or “dropping out,” some activations during training. This causes the network to learn more robust representations that generalize better to novel data.

However, dropout has a few limitations. For instance, it requires a careful choice of dropout rate, and once the dropout rate is set, it remains constant throughout the training process. Moreover, dropout does not always provide the same improvement in performance across different layers.

To address these shortcomings, a team of researchers introduced the idea of variational dropout, which is a more flexible and adaptive form of dropout. Variational dropout allows the dropout rate to be learned, rather than being set as a hyperparameter. Furthermore, it can be applied to different layers independently, which provides more control over the regularization process.

What is Variational Dropout?

Variational dropout is an extension of the standard dropout technique that adds a probabilistic interpretation to dropout. Rather than randomly setting activations to zero, as in traditional dropout, variational dropout learns a Gaussian distribution over activations and randomly samples from it during training.

The learned distribution is parameterized by a mean and a standard deviation, which are computed based on the input to each layer. This means that the dropout rate is no longer fixed but can vary based on the input to the layer and the learned distribution.

By learning the dropout rate, the model can adapt to the complexity of the data and the task it is trained on. Moreover, the model can use the learned distribution as a form of uncertainty estimation, which can help it make better predictions on novel data.

How Does Variational Dropout Work?

Like standard dropout, variational dropout is implemented during training and disabled during testing. During training, a dropout mask is applied to the activations of each layer, based on the learned Gaussian distribution. The dropout mask is randomly sampled from the distribution and applied to the activations, effectively dropping out some of them.

While the mask is drawn independently for each sample and each layer, the same mask is applied to all inputs of each layer. This ensures that the same dropout pattern is applied to all inputs, which is important for regularization purposes.

The dropout mask is expressed as a binary vector of the same size as the input to the layer. Each element of the vector is set to 0 or 1, depending on whether the activation should be dropped out or kept. The probability of an element being set to 1 is equal to the mean of the Gaussian distribution over activations.

Since the mean of the Gaussian is learned, it can vary based on the input to each layer. For instance, if the input is noisy or complex, the mean can be increased to drop out more activations and regularize the layer. On the other hand, if the input is clear and simple, the mean can be decreased to preserve more of the activations and avoid underfitting.

Variational Dropout vs. Standard Dropout

While variational dropout and standard dropout share some similarities, they differ in several key ways:

  • Variational dropout learns a Gaussian distribution over activations, while standard dropout randomly drops out activations based on a fixed dropout rate.
  • Variational dropout can vary the dropout rate for each layer and each input, while standard dropout uses the same dropout rate for all layers and inputs.
  • Variational dropout enables the use of the dropout distribution as a form of uncertainty estimation, while standard dropout does not.

These differences make variational dropout a more powerful and flexible regularization technique than standard dropout.

Applications of Variational Dropout

Variational dropout has been applied to a wide range of deep learning tasks and models, including:

  • Image classification
  • Object detection
  • Speech recognition
  • Reinforcement learning
  • Natural language processing
  • Generative models

In each of these applications, variational dropout has demonstrated its effectiveness and flexibility as a regularization technique. Moreover, its ability to provide uncertainty estimates has been shown to enhance the performance and reliability of the models.


Variational dropout is a potent regularization technique that offers a more flexible and adaptive alternative to standard dropout. By learning a Gaussian distribution over activations and sampling from it during training, variational dropout can adapt to the complexity of the data and enable uncertainty estimation.

Given its many benefits, variational dropout is likely to become a standard technique in deep learning, with widespread applications across a range of tasks and models.