A Unified Approach to Count-Based Weakly-Supervised Learning
Vinay Shukla Zhe Zeng Kareem Ahmed Guy Van den Broeck
High-quality labels are often very scarce, whereas unlabeled data with
inferred weak labels occurs more naturally. In many cases, these weak labels
dictate the frequency of each respective class over a set of instances. In this
paper, we develop a unified approach to learning from such weakly-labeled data,
which we call count-based weakly-supervised learning. At the heart of our
approach is the ability to compute the probability of exactly k out of n
outputs being set to true. This computation is differentiable, exact, and
efficient. Building upon the previous computation, we derive a count loss
penalizing the model for deviations in its distribution from an arithmetic
constraint defined over label counts. We evaluate our approach on three common
weakly-supervised learning paradigms and observe that our proposed approach
achieves state-of-the-art or highly competitive results across all three of the
paradigms.