Online Black-Box Confidence Estimation of Deep Neural Networks

Fabian Woitschek，Georg Schneider

[ZF Friedrichshafen AG]

深度神经网络的在线黑箱置信度估计

要点：

  1. 自动驾驶（AD）和高级驾驶员辅助系统（ADAS）越来越多地利用深度神经网络（DNN）来改善感知或规划。然而，当推断期间的数据分布与训练期间的数据分配不同时，DNN非常脆弱。这是在部分未知环境（如ADAS）中部署时面临的挑战。同时，即使分类可靠性降低，DNN的标准置信度仍然很高。这是有问题的，因为下面的运动控制算法认为显然有信心的预测是可靠的，即使它可能相当错误。
  2. 为了减少这个问题，需要实时能力的置信估计，以更好地与DNN分类的实际可靠性一致。此外，需要进行黑箱置信度估计，以便能够将外部开发的组件均匀地包含到整个系统中。本论文探索了这个用例，并引入了邻域置信度（NHC），它估计了用于分类的任意DNN的置信度。该度量可用于黑盒系统，因为只需要前1类输出，不需要访问梯度、训练数据集或保持验证数据集。

一句话总结：

对不同数据分布（包括小的域内分布偏移、域外数据或对抗性攻击）的评估表明，NHC在低数据状态下的在线白盒置信度估计方法表现更好或与之相当，这是实时AD/ADAS所需的。同样，NHC与对抗性训练的相互作用值得详细研究，因为对抗性训练会导致训练数据样本周围的决策区域增加和同质化。

Autonomous driving (AD) and advanced driver assistance systems (ADAS) increasingly utilize deep neural networks (DNNs) for improved perception or planning. Nevertheless, DNNs are quite brittle when the data distribution during inference deviates from the data distribution during training. This represents a challenge when deploying in partly unknown environments like in the case of ADAS. At the same time, the standard confidence of DNNs remains high even if the classification reliability decreases. This is problematic since following motion control algorithms consider the apparently confident prediction as reliable even though it might be considerably wrong. To reduce this problem real-time capable confidence estimation is required that better aligns with the actual reliability of the DNN classification. Additionally, the need exists for black-box confidence estimation to enable the homogeneous inclusion of externally developed components to an entire system. In this work we explore this use case and introduce the neighborhood confidence (NHC) which estimates the confidence of an arbitrary DNN for classification. The metric can be used for black-box systems since only the top-1 class output is required and does not need access to the gradients, the training dataset or a hold-out validation dataset. Evaluation on different data distributions, including small in-domain distribution shifts, out-of-domain data or adversarial attacks, shows that the NHC performs better or on par with a comparable method for online white-box confidence estimation in low data regimes which is required for real-time capable AD/ADAS.

https://arxiv.org/pdf/2302.13578.pdf

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