论文标题
部分可观测时空混沌系统的无模型预测
CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition
论文作者
论文摘要
神经网络被广泛采用，但是域知识的整合仍未得到充分利用。我们建议将域知识整合到相处的面部运动作为损失功能的限制，以增强神经网络的训练以识别识别。由于共同汇集模式在数据集中往往相似，因此应用我们的方法可能会导致模型的概括性更高，并且过度拟合的风险较低。我们通过显示各种数据集的跨数据库测试的性能提高来证明这一点。我们还展示了我们的方法在校准神经网络对不同面部表情的适用性。
Neural networks are widely adopted, yet the integration of domain knowledge is still underutilized. We propose to integrate domain knowledge about co-occurring facial movements as a constraint in the loss function to enhance the training of neural networks for affect recognition. As the co-ccurrence patterns tend to be similar across datasets, applying our method can lead to a higher generalizability of models and a lower risk of overfitting. We demonstrate this by showing performance increases in cross-dataset testing for various datasets. We also show the applicability of our method for calibrating neural networks to different facial expressions.
