Contents
How is training with noise equivalent to regularization?
Previous work has shown that such training with noise is equivalent to a form of regularization in which an extra term is added to the error function. — Training with Noise is Equivalent to Tikhonov Regularization, 2008. In effect, adding noise expands the size of the training dataset.
How is noise used to improve generalization error?
One approach to improving generalization error and to improving the structure of the mapping problem is to add random noise. Many studies […] have noted that adding small amounts of input noise (jitter) to the training data often aids generalization and fault tolerance.
What happens when too much noise is added to a mapping function?
The amount of noise added (eg. the spread or standard deviation) is a configurable hyperparameter. Too little noise has no effect, whereas too much noise makes the mapping function too challenging to learn.
What happens when you add noise to a training dataset?
— Training with Noise is Equivalent to Tikhonov Regularization, 2008. In effect, adding noise expands the size of the training dataset. Each time a training sample is exposed to the model, random noise is added to the input variables making them different every time it is exposed to the model.
What kind of noise is used in training?
The most common type of noise used during training is the addition of Gaussian noise to input variables. Gaussian noise, or white noise, has a mean of zero and a standard deviation of one and can be generated as needed using a pseudorandom number generator.
How is L2 loss function sensitive to outliers?
On the contrary L2 loss function will try to adjust the model according to these outlier values, even on the expense of other samples. Hence, L2 loss function is highly sensitive to outliers in the dataset. We’ll see how outliers can affect the performance of a regression model.