  The superior performance of some of today's state-of-the-art deep learning
models is to some extent owed to extensive (self-)supervised contrastive
pretraining on large-scale datasets. In contrastive learning, the network is
presented with pairs of positive (similar) and negative (dissimilar) datapoints
and is trained to find an embedding vector for each datapoint, i.e., a
representation, which can be further fine-tuned for various downstream tasks.
In order to safely deploy these models in critical decision-making systems, it
is crucial to equip them with a measure of their uncertainty or reliability.
However, due to the pairwise nature of training a contrastive model, and the
lack of absolute labels on the output (an abstract embedding vector), adapting
conventional uncertainty estimation techniques to such models is non-trivial.
In this work, we study whether the uncertainty of such a representation can be
quantified for a single datapoint in a meaningful way. In other words, we
explore if the downstream performance on a given datapoint is predictable,
directly from its pre-trained embedding. We show that this goal can be achieved
by directly estimating the distribution of the training data in the embedding
space and accounting for the local consistency of the representations. Our
experiments show that this notion of uncertainty for an embedding vector often
strongly correlates with its downstream accuracy.
