论文标题
比较学习：用于几次学习的双重注意网络
Compare learning: bi-attention network for few-shot learning
论文作者
论文摘要
很少有标记数据的学习是视觉识别的关键挑战，因为深层神经网络倾向于仅使用一些样本过度拟合。少数几种称为公制学习的学习方法之一是通过首先学习深距离度量标准来确定一对图像是否属于同一类别，然后将受过训练的指标应用于其他带有有限标签的测试集的实例，以解决这一挑战。该方法充分利用了少数样品中的大部分，并有效地限制了过度拟合。但是，现有的度量网络通常采用线性分类器或卷积神经网络（CNN），这些分类器（CNN）不够精确，无法在全球范围内捕获向量之间的细微差异。在本文中，我们提出了一种名为“双重注意”网络的新型方法，以比较这些实例，该方法可以精确，全球和有效地衡量实例嵌入之间的相似性。我们验证模型对两个基准的有效性。实验表明，我们实现的方法提高了基线模型的准确性和收敛速度。
Learning with few labeled data is a key challenge for visual recognition, as deep neural networks tend to overfit using a few samples only. One of the Few-shot learning methods called metric learning addresses this challenge by first learning a deep distance metric to determine whether a pair of images belong to the same category, then applying the trained metric to instances from other test set with limited labels. This method makes the most of the few samples and limits the overfitting effectively. However, extant metric networks usually employ Linear classifiers or Convolutional neural networks (CNN) that are not precise enough to globally capture the subtle differences between vectors. In this paper, we propose a novel approach named Bi-attention network to compare the instances, which can measure the similarity between embeddings of instances precisely, globally and efficiently. We verify the effectiveness of our model on two benchmarks. Experiments show that our approach achieved improved accuracy and convergence speed over baseline models.
