论文标题
基于变分编码器的可靠分类
Variational Encoder-based Reliable Classification
论文作者
论文摘要
机器学习模型提供了统计上令人印象深刻的结果，这些结果可能是单独不可靠的。为了提供可靠性，我们提出了一个认知分类器（EC），可以使用培训数据集的支持以及重建质量来提供其信念的理由。我们的方法基于修改的变分自动编码器，这些自动编码器可以识别语义上有意义的低维空间，在这些空间中，感知上相似的实例在$ \ ell_2 $ distance中也接近。我们的结果表明，与基于软马克斯的阈值的基线相比，对预测的可靠性和对对抗性攻击的样品的可靠性得到了改善。
Machine learning models provide statistically impressive results which might be individually unreliable. To provide reliability, we propose an Epistemic Classifier (EC) that can provide justification of its belief using support from the training dataset as well as quality of reconstruction. Our approach is based on modified variational auto-encoders that can identify a semantically meaningful low-dimensional space where perceptually similar instances are close in $\ell_2$-distance too. Our results demonstrate improved reliability of predictions and robust identification of samples with adversarial attacks as compared to baseline of softmax-based thresholding.
