Toward Improving Stochastic Neural Network Robustness via Arbitrary Distribution Injection.
A novel SNN named arbitrary distribution injection is proposed, which enables distribution modeling from nonpredefined, data-dependent distributions and introduces a conditional stochastic feature mapping mechanism to model feature distributions, together with a theoretically grounded variance-regularization loss.