Conformal prediction is a distribution-free technique for establishing valid prediction intervals. Although conventionally people conduct conformal prediction in the output space, this is not the only possibility. In this paper, we propose feature conformal prediction, which extends the scope of conformal prediction to semantic feature spaces by leveraging the inductive bias of deep representation learning. From a theoretical perspective, we demonstrate that feature conformal prediction provably outperforms regular conformal prediction under mild assumptions. Our approach could be combined with not only vanilla conformal prediction, but also other adaptive conformal prediction methods. Apart from experiments on existing predictive inference benchmarks, we also demonstrate the state-of-the-art performance of the proposed methods on large-scale tasks such as ImageNet classification and Cityscapes image segmentation.

翻译：非正式预测是建立有效预测间隔的无分配技术。 虽然通常人们在产出空间进行一致预测,但这不是唯一的可能。 在本文中,我们提出符合特性预测,通过利用深层表述学习的感应偏差,将符合性预测的范围扩大到语体特征空间。从理论角度看,我们证明符合性预测在轻度假设下优于正常的符合性预测。我们的方法不仅可以结合香草符合性预测,还可以结合其他适应性符合性预测方法。除了对现有预测推论基准的实验外,我们还展示了如图像网络分类和城市图象分割等大规模任务的拟议方法的最新表现。</s>