I have a stupid question,
Is anyone knows that what should be the form of loss function in an Denoising Autoencoder?
should it be like below?;
loss = criterion (model (noisy_data),noise_less_data)
basically model (noisy_data) is the model will be trained with inputs that are corrupted data and loss function calculates the difference (here MSE) between output of the model and the data that are not noisy ?
In this way that makes no sense to me. because if we already have access to noiseless data, then what’s the point of building up a denoising autoencoder?