论文标题
通过语义驱动的细心学习对干净和嘈杂的样本的学习
Semantics-driven Attentive Few-shot Learning over Clean and Noisy Samples
论文作者
论文摘要
在过去的几年中，很少有射击学习（FSL）引起了极大的关注，以最大程度地降低对标记培训示例的依赖。 FSL固有的困难是处理歧义是由于每班训练样本太少而导致的歧义。为了应对FSL中的这一基本挑战，我们旨在训练可以利用有关新颖类的语义知识来指导分类器综合过程的元学习模型。特别是，我们提出了语义条件的特征注意力和样本注意机制，以估计表示维度和训练实例的重要性。我们还研究了FSL中样品噪声的问题，在更现实和不完美的设置中使用元学习者的利用。我们的实验结果证明了带有和没有样品噪声的拟议语义FSL模型的有效性。
Over the last couple of years few-shot learning (FSL) has attracted great attention towards minimizing the dependency on labeled training examples. An inherent difficulty in FSL is the handling of ambiguities resulting from having too few training samples per class. To tackle this fundamental challenge in FSL, we aim to train meta-learner models that can leverage prior semantic knowledge about novel classes to guide the classifier synthesis process. In particular, we propose semantically-conditioned feature attention and sample attention mechanisms that estimate the importance of representation dimensions and training instances. We also study the problem of sample noise in FSL, towards the utilization of meta-learners in more realistic and imperfect settings. Our experimental results demonstrate the effectiveness of the proposed semantic FSL model with and without sample noise.
