The Limited Multi-Label Projection Layer
This addresses multi-label learning with incomplete label information, offering a novel method for optimizing top-k recall, but it is incremental as it builds on existing top-k classification techniques.
The paper tackled the problem of multi-label predictions limited to exactly k labels by proposing the Limited Multi-Label (LML) projection layer, resulting in improved accuracy with negligible computational overhead in tasks like top-k CIFAR-100 classification and scene graph generation.
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.