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Virtual Adversarial Training is an effective regularization technique which has given good results in supervised learning, semi-supervised learning, and unsupervised clustering.

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Virtual adversarial training has been used for:

  1. Improving supervised learning performance
  2. Semi-supervised learning
  3. Deep unsupervised clustering

There are several regularization techniques which prevent overfitting and help the model generalize better for unseen examples. Regularization helps the model parameters to be less dependent on the training data. The two most commonly used regularization techniques are Dropout and L1/L2 regularization.

In L1/L2 regularization, we add a loss term which tries to reduce the L1 norm or the L2 norm of the weights matrix. Small value of weights would result in simpler models which are less prone to overfitting. …


Divam Gupta

I’m a research fellow at Microsoft Research. I blog about Machine Learning and Deep Learning. My personal website :

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