While many deep learning methods have seen significant success in tackling the problem of domain adaptation and few-shot learning separately, far fewer methods are able to jointly tackle both problems in Cross-Domain Few-Shot Learning (CD-FSL). This problem is exacerbated under sharp domain shifts that typify common computer vision applications. In this paper, we present a novel, flexible and effective method to address the CD-FSL problem. Our method, called Score-based Meta Transfer-Learning (SB-MTL), combines transfer-learning and meta-learning by using a MAML-optimized feature encoder and a score-based Graph Neural Network. First, we have a feature encoder with specific layers designed to be fine-tuned. To do so, we apply a first-order MAML algorithm to find good initializations. Second, instead of directly taking the classification scores after fine-tuning, we interpret the scores as coordinates by mapping the pre-softmax classification scores onto a metric space. Subsequently, we apply a Graph Neural Network to propagate label information from the support set to the query set in our score-based metric space. We test our model on the Broader Study of Cross-Domain Few-Shot Learning (BSCD-FSL) benchmark, which includes a range of target domains with highly varying dissimilarity to the miniImagenet source domain. We observe significant improvements in accuracy across 5, 20 and 50 shot, and on the four target domains. In terms of average accuracy, our model outperforms previous transfer-learning methods by 5.93% and previous meta-learning methods by 14.28%.
翻译:虽然许多深层次的学习方法在解决域适应问题方面取得了显著成功,但分别解决域适应问题和少见的学习方面取得了显著成功,但在跨域少热学习(CD-FSL)中,能够共同解决这两个问题的方法却少得多。这个问题在典型通用计算机视野应用程序的尖锐域变换中更加严重。在本文中,我们展示了一种新颖、灵活和有效的方法来解决CD-FSL问题。我们的方法叫做基于分数的元转移学习学习(SB-MTL),将转移-学习和元学习结合起来,方法是使用MAML-优化功能编码器和基于分数的图表神经网络。首先,我们有一个功能编码编码器,其具体的层设计要精确。为了这样做,我们应用了第一个级的MAMLL算法来寻找良好的初始化。第二,而不是在微调后直接进行分类分分分,我们将这些评分作为坐标,方法是在14个空间绘制软度的分类模型。随后,我们用图表神经网络将标签信息传播到我们之前的分数-CD50级指标网络的精确度指标定位,5,我们用不同的基准域域的精确度的精确范围,我们用一个模型测试了我们用一个模型到一个不同的模型,在不同的域域域域域域的模型进行。