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.

本文提出了 Limited Multi-Label（LML）投影层作为端到端学习系统的一个新的原语操作，这个层提供了一个概率建模有 k 个标签的多标签预测的方法，并提供了 LML 层的前向和反向传递的有效方法，我们在 top-k CIFAR-100 分类和场景图生成上评估 LML 层，并表明 LML 层增加的计算开销可忽略不计，严格提高了模型的表示能力和准确性，同时重新考虑了截断的 top-k 熵方法作为 top-k 分类的竞争基线。

受限多标签投影层

The Limited Multi-Label Projection Layer

Despite the increasing research interest in end-to-end learning systems for
speech emotion recognition, conventional systems either suffer from the
overfitting due in part to the limited training data, or do not explicitly
consider the different contributions of automatically learnt representations
for a specific task. In this contribution, we propose a novel end-to-end
framework which is enhanced by learning other auxiliary tasks and an attention
mechanism. That is, we jointly train an end-to-end network with several
different but related emotion prediction tasks, i.e., arousal, valence, and
dominance predictions, to extract more robust representations shared among
various tasks than traditional systems with the hope that it is able to relieve
the overfitting problem. Meanwhile, an attention layer is implemented on top of
the layers for each task, with the aim to capture the contribution distribution
of different segment parts for each individual task. To evaluate the
effectiveness of the proposed system, we conducted a set of experiments on the
widely used database IEMOCAP. The empirical results show that the proposed
systems significantly outperform corresponding baseline systems.

本文提出了一种增强的端到端框架，通过学习其他辅助任务和关注机制来获取更加稳健和更好地共享表示，以缓解传统系统中由于有限训练数据而导致的过拟合问题，提高语音情感识别性能。