Contrastive approaches to representation learning have recently shown great
promise. In contrast to generative approaches, these contrastive models learn a
deterministic encoder with no notion of uncertainty or confidence. In this
paper, we introduce a simple approach based on "contrasting distributions" that
learns to assign uncertainty for pretrained contrastive representations. In
particular, we train a deep network from a representation to a distribution in
representation space, whose variance can be used as a measure of confidence. In
our experiments, we show that this deep uncertainty model can be used (1) to
visually interpret model behavior, (2) to detect new noise in the input to
deployed models, (3) to detect anomalies, where we outperform 10 baseline
methods across 11 tasks with improvements of up to 14% absolute, and (4) to
classify out-of-distribution examples where our fully unsupervised model is
competitive with supervised methods.

该论文介绍了一种基于 “对比分布” 的简单方法，该方法通过训练一个从表示到分布的深度网络，并使用方差来衡量置信度，从而赋予预训练的对比表示学习不确定性的能力。在实验中，作者展示了该深度不确定性模型可用于视觉解释模型行为、检测已部署模型中的新噪声和异常，其中在 11 个任务中，对比 10 种基线方法改进了 14％以上，同样可以用于分类超出分布的示例，其性能可与有监督方法竞争。