Traditional neural networks trained using point-based maximum likelihood estimation are deterministic models and have exhibited near-human performance in many image classification tasks. However, their insistence on representing network parameters with point-estimates renders them incapable of capturing all possible combinations of the weights; consequently, resulting in a biased predictor towards their initialisation. Most importantly, these deterministic networks are inherently unable to provide any uncertainty estimate for their prediction which is highly sought after in many critical application areas. On the other hand, Bayesian neural networks place a probability distribution on network weights and give a built-in regularisation effect making these models able to learn well from small datasets without overfitting. These networks provide a way of generating posterior distribution which can be used for model’s uncertainty estimation. However, Bayesian estimation is computationally very expensive since it greatly widens the parameter space. This paper proposes a hybrid convolutional neural network which combines high accuracy of deterministic models with posterior distribution approximation of Bayesian neural networks. This hybrid architecture is validated on 13 publicly available benchmark classification datasets from a wide range of domains and different modalities like natural scene images, medical images, and time-series. Our results show that the proposed hybrid approach performs better than both deterministic and Bayesian methods in terms of classification accuracy and also provides an estimate of uncertainty for every prediction. We further employ this uncertainty to filter out unconfident predictions and achieve significant additional gain in accuracy for the remaining predictions.

中文翻译：

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使用确定性和概率卷积神经网络之间的混合进行置信分类

使用基于点的最大似然估计训练的传统神经网络是确定性模型，并且在许多图像分类任务中表现出接近人类的性能。然而，他们坚持用点估计来表示网络参数，这使他们无法捕获所有可能的权重组合；因此，导致对其初始化的预测有偏差。最重要的是，这些确定性网络本质上无法为其预测提供任何不确定性估计，这在许多关键应用领域受到高度追捧。另一方面，贝叶斯神经网络将概率分布放在网络权重上，并提供内置的正则化效果，使这些模型能够从小数据集很好地学习而不会过度拟合。这些网络提供了一种生成后验分布的方法，可用于模型的不确定性估计。然而，贝叶斯估计在计算上非常昂贵，因为它极大地拓宽了参数空间。本文提出了一种混合卷积神经网络，它将确定性模型的高精度与贝叶斯神经网络的后验分布近似相结合。这种混合架构在来自广泛领域和不同模式（如自然场景图像、医学图像和时间序列）的 13 个公开可用的基准分类数据集上得到验证。我们的结果表明，所提出的混合方法在分类准确性方面比确定性方法和贝叶斯方法都要好，并且还为每个预测提供了对不确定性的估计。