In state-of-the-art deep learning for object recognition, SoftMax and Sigmoid
functions are most commonly employed as the predictor outputs. Such layers
often produce overconfident predictions rather than proper probabilistic
scores, which can thus harm the decision-making of `critical' perception
systems applied in autonomous driving and robotics. Given this, the experiments
in this work propose a probabilistic approach based on distributions calculated
out of the Logit layer scores of pre-trained networks. We demonstrate that
Maximum Likelihood (ML) and Maximum a-Posteriori (MAP) functions are more
suitable for probabilistic interpretations than SoftMax and Sigmoid-based
predictions for object recognition. We explore distinct sensor modalities via
RGB images and LiDARs (RV: range-view) data from the KITTI and Lyft Level-5
datasets, where our approach shows promising performance compared to the usual
SoftMax and Sigmoid layers, with the benefit of enabling interpretable
probabilistic predictions. Another advantage of the approach introduced in this
paper is that the ML and MAP functions can be implemented in existing trained
networks, that is, the approach benefits from the output of the Logit layer of
pre-trained networks. Thus, there is no need to carry out a new training phase
since the ML and MAP functions are used in the test/prediction phase.

本文探讨了基于概率的方式，采用已预训练网络的 Logit 层得分计算分布来进行物体识别，表明与 SoftMax 和 Sigmoid 相比，最大似然和最大后验函数更适合进行这种概率化解释，实现了良好的性能和可解释性。同时，由于这种方法可以在现有训练好的网络上实施，无需再进行训练。

自动驾驶感知减少过度自信的预测

Reducing Overconfidence Predictions for Autonomous Driving Perception

Modern machine learning models are often trained on examples with noisy
labels that hurt performance and are hard to identify. In this paper, we
provide an empirical study showing that a simple $k$-nearest neighbor-based
filtering approach on the logit layer of a preliminary model can remove
mislabeled training data and produce more accurate models than many recently
proposed methods. We also provide new statistical guarantees into its efficacy.

本文通过实证研究表明，在初步模型的 logit 层上实施简单的 k 近邻过滤方法能够去除错误标记的训练数据，比许多最近提出的方法产生更准确的模型，并提供了新的统计保证。