论文标题
嘿，如果您的面部情绪不确定，则应使用贝叶斯神经网络！
Hey Human, If your Facial Emotions are Uncertain, You Should Use Bayesian Neural Networks!
论文作者
论文摘要
面部情绪识别是对人类情绪在面部图像中进行分类的任务。由于较高的不确定性和视觉歧义，这是一项艰巨的任务。文献的很大一部分旨在通过提高此任务的准确性来显示进度，但这忽略了任务中固有的不确定性和歧义。在本文中，我们表明，使用MC-Dropout，Mc-Dropconnect或合奏进行了近似，贝叶斯神经网络能够模拟面部情绪识别的不良不确定性，并产生更接近人类期望的输出概率。我们还表明，由于可以认为正确的多个类，这会激发未来的工作，因此校准指标显示了此任务的奇怪行为。我们认为，我们的工作将激励其他研究人员从古典和贝叶斯神经网络转移。
Facial emotion recognition is the task to classify human emotions in face images. It is a difficult task due to high aleatoric uncertainty and visual ambiguity. A large part of the literature aims to show progress by increasing accuracy on this task, but this ignores the inherent uncertainty and ambiguity in the task. In this paper we show that Bayesian Neural Networks, as approximated using MC-Dropout, MC-DropConnect, or an Ensemble, are able to model the aleatoric uncertainty in facial emotion recognition, and produce output probabilities that are closer to what a human expects. We also show that calibration metrics show strange behaviors for this task, due to the multiple classes that can be considered correct, which motivates future work. We believe our work will motivate other researchers to move away from Classical and into Bayesian Neural Networks.
