This is useful to mitigate overfitting (you could see it as a form of random data augmentation). Gaussian Noise (GS) is a natural choice as corruption process for real valued inputs. As it is a regularization layer, it is only active at training time.
layer_gaussian_noise(object, stddev, seed = NULL, ...)What to compose the new Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by layer_input()).
The return value depends on object. If object is:
missing or NULL, the Layer instance is returned.
a Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object) is returned.
float, standard deviation of the noise distribution.
Integer, optional random seed to enable deterministic behavior.
standard layer arguments.
Arbitrary. Use the keyword argument input_shape (list
of integers, does not include the samples axis) when using this layer as
the first layer in a model.
Same shape as input.
Other noise layers:
layer_alpha_dropout(),
layer_gaussian_dropout()