  We introduce implicit Bayesian neural networks, a simple and scalable
approach for uncertainty representation in deep learning. Standard Bayesian
approach to deep learning requires the impractical inference of the posterior
distribution over millions of parameters. Instead, we propose to induce a
distribution that captures the uncertainty over neural networks by augmenting
each layer's inputs with latent variables. We present appropriate input
distributions and demonstrate state-of-the-art performance in terms of
calibration, robustness and uncertainty characterisation over large-scale,
multi-million parameter image classification tasks.
