Bayesian deep learning approaches assume model parameters to be latent random variables and infer posterior distributions to quantify uncertainty, increase safety and trust, and prevent overconfident and unpredictable behavior. However, weight-space priors are model-specific, can be difficult to interpret and are hard to specify. Instead, we apply a Dirichlet prior in predictive space and perform approximate function-space variational inference. To this end, we interpret conventional categorical predictions from stochastic neural network classifiers as samples from an implicit Dirichlet distribution. By adapting the inference, the same function-space prior can be combined with different models without affecting model architecture or size. We illustrate the flexibility and efficacy of such a prior with toy experiments and demonstrate scalability, improved uncertainty quantification and adversarial robustness with large-scale image classification experiments.

翻译：贝叶斯深度学习方法将模型参数视为潜在随机变量，并通过推断后验分布来量化不确定性、增强安全性与可信度，同时避免过度自信及不可预测的行为。然而，权重空间先验具有模型特异性，难以解释且不易指定。为此，我们在预测空间中应用狄利克雷先验，并执行近似的函数空间变分推断。通过将随机神经网络分类器输出的常规分类预测解释为隐式狄利克雷分布的采样，我们在调整推断方法后，可使相同的函数空间先验与不同模型结合，且不影响模型架构或规模。我们通过玩具实验展示了此类先验的灵活性与有效性，并通过大规模图像分类实验验证了其可扩展性、改进的不确定性量化能力以及对抗鲁棒性。