The Limited Multi-Label Projection Layer

Abstract

We propose the Limited Multi-Label (LML) projection layer as a new primitive operation for end-to-end learning systems. The LML layer provides a probabilistic way of modeling multi-label predictions limited to having exactly k labels. We derive efficient forward and backward passes for this layer and show how the layer can be used to optimize the top-k recall for multi-label tasks with incomplete label information. We evaluate LML layers on top-k CIFAR-100 classification and scene graph generation. We show that LML layers add a negligible amount of computational overhead, strictly improve the model's representational capacity, and improve accuracy. We also revisit the truncated top-k entropy method as a competitive baseline for top-k classification.

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• Ranking via Sinkhorn Propagation
• Discovering objects and their relations from entangled scene representations
• Sparse Support Vector Infinite Push
• Structured Prediction with Projection Oracles
• Learning with Fenchel-Young Losses

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