Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
This addresses the challenge of enforcing label count limits in deep learning for structured prediction, which is incremental but improves performance in multi-label tasks.
The paper tackles the problem of incorporating cardinality constraints into deep structured prediction models for multi-label classification, achieving state-of-the-art results on benchmarks.
Many machine learning problems require the prediction of multi-dimensional labels. Such structured prediction models can benefit from modeling dependencies between labels. Recently, several deep learning approaches to structured prediction have been proposed. Here we focus on capturing cardinality constraints in such models. Namely, constraining the number of non-zero labels that the model outputs. Such constraints have proven very useful in previous structured prediction approaches, but it is a challenge to introduce them into a deep learning framework. Here we show how to do this via a novel deep architecture. Our approach outperforms strong baselines, achieving state-of-the-art results on multi-label classification benchmarks.