让机器识别不确定时主动求助，提升人脸识别准确率。
Elements of Active Continuous Learning and Uncertainty Self-Awareness: a Narrow Implementation for Face and Facial Expression Recognition
- 用监督网络监控主网络激活模式，判断预测不确定性。
- 不确定时触发主动学习，请求人工标注，提升模型鲁棒性。
- 适合需要高可靠性的实时人脸识别场景。
反思自身推理过程并在性能不达标时进行修正，或许是智能的核心特征。然而，这种高级抽象概念也可在狭义机器学习算法中实现。本文提出一种自知机制：通过一个监督型人工神经网络（ANN）观察另一底层卷积神经网络（CNN）集成模型的激活模式，以识别其高不确定性，进而评估预测可信度。该底层模型用于人脸与表情识别任务。自知网络配备记忆区域，存储过往表现信息，并在训练中调整可学习参数以优化性能。当可信度判定为低时，系统进入主动学习模式，赋予算法一定自主性，在高不确定性情境下主动请求人类协助。
原文摘要 · Abstract (English)
Reflection on one's thought process and making corrections to it if there exists dissatisfaction in its performance is, perhaps, one of the essential traits of intelligence. However, such high-level abstract concepts mandatory for Artificial General Intelligence can be modelled even at the low level of narrow Machine Learning algorithms. Here, we present the self-awareness mechanism emulation in the form of a supervising artificial neural network (ANN) observing patterns in activations of another underlying ANN in a search for indications of the high uncertainty of the underlying ANN and, therefore, the trustworthiness of its predictions. The underlying ANN is a convolutional neural network (CNN) ensemble employed for face recognition and facial expression tasks. The self-awareness ANN has a memory region where its past performance information is stored, and its learnable parameters are adjusted during the training to optimize the performance. The trustworthiness verdict triggers the active learning mode, giving elements of agency to the machine learning algorithm that asks for human help in high uncertainty and confusion conditions.
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