Regulating latent variables or latent features can improve the generalizability of classifier and lower the error bound.
Regulating latent variables is essentially decrease the entropy of latent variables. There are some common tricks to decrease the entropy of latent variables, for example,
- dropout
- weight decay
- add random noise to the latent variables in VAE and GAN.
- add random perturbation to model parameters
For theoretical proof, please refer to here.