论文标题
零射击的偏见意识学习可见和看不见的
Bias-Awareness for Zero-Shot Learning the Seen and Unseen
论文作者
论文摘要
广义的零射门学习识别出可见和看不见的类别的输入。但是，现有方法往往会偏向训练期间所见的课程。在本文中，我们努力减轻这种偏见。我们建议一个偏见的学习者将输入映射到一个语义嵌入空间，以进行广义零拍学习。在训练过程中，该模型学会了以温度缩放为嵌入空间中的实值类原型，而基于边距的双向熵项则定期可见和看不见的概率。依靠实价的语义嵌入空间提供了一种多功能方法，因为该模型可以在不同类型的语义信息上为可见的类和看不见的类操作。实验是在四个基准上进行的，用于广义零射击学习，并证明了所提出的偏见分类器的好处，无论是作为独立方法还是与生成的特征结合使用。
Generalized zero-shot learning recognizes inputs from both seen and unseen classes. Yet, existing methods tend to be biased towards the classes seen during training. In this paper, we strive to mitigate this bias. We propose a bias-aware learner to map inputs to a semantic embedding space for generalized zero-shot learning. During training, the model learns to regress to real-valued class prototypes in the embedding space with temperature scaling, while a margin-based bidirectional entropy term regularizes seen and unseen probabilities. Relying on a real-valued semantic embedding space provides a versatile approach, as the model can operate on different types of semantic information for both seen and unseen classes. Experiments are carried out on four benchmarks for generalized zero-shot learning and demonstrate the benefits of the proposed bias-aware classifier, both as a stand-alone method or in combination with generated features.
