OOD detection has become more pertinent with advances in network design and increased task complexity. Identifying which parts of the data a given network is misclassifying has become as valuable as the network's overall performance. We can compress the model with quantization, but it suffers minor performance loss. The loss of performance further necessitates the need to derive the confidence estimate of the network's predictions. In line with this thinking, we introduce an Uncertainty Quantification(UQ) technique to quantify the uncertainty in the predictions from a pre-trained vision model. We subsequently leverage this information to extract valuable predictions while ignoring the non-confident predictions. We observe that our technique saves up to 80% of ignored samples from being misclassified. The code for the same is available here.

翻译：OOD检测随着网络设计的进步和任务复杂性的增加而变得愈发重要。识别给定网络对数据中哪些部分存在误分类，已变得与网络整体性能同等重要。我们可以通过量化压缩模型，但这一过程会带来轻微的性能损失。性能损失进一步凸显了获取网络预测置信度估计的必要性。基于这一思路，我们引入了一种不确定性量化（UQ）技术，用于量化预训练视觉模型预测中的不确定性。随后，我们利用这一信息提取有价值的预测，同时忽略不可靠的预测。实验表明，我们的技术能够使高达80%的忽略样本免于误分类。相关代码可在此处获取。