Few-shot learning (FSL) targets at generalization of vision models towards unseen tasks without sufficient annotations. Despite the emergence of a number of few-shot learning methods, the sample selection bias problem, i.e., the sensitivity to the limited amount of support data, has not been well understood. In this paper, we find that this problem usually occurs when the positions of support samples are in the vicinity of task centroid -- the mean of all class centroids in the task. This motivates us to propose an extremely simple feature transformation to alleviate this problem, dubbed Task Centroid Projection Removing (TCPR). TCPR is applied directly to all image features in a given task, aiming at removing the dimension of features along the direction of the task centroid. While the exact task centroid cannot be accurately obtained from limited data, we estimate it using base features that are each similar to one of the support features. Our method effectively prevents features from being too close to the task centroid. Extensive experiments over ten datasets from different domains show that TCPR can reliably improve classification accuracy across various feature extractors, training algorithms and datasets. The code has been made available at https://github.com/KikimorMay/FSL-TCBR.
翻译:微小的学习(FSL)目标是将视觉模型推广到没有足够说明的无形任务。尽管出现了一些微小的学习方法,但抽样选择偏差问题,即对数量有限的支助数据的敏感性,并没有得到很好理解。在本文中,我们发现,这个问题通常发生在支助样品的位置接近任务中所有类类中子机器人的平均值 -- -- 任务中所有类中子体的平均值 -- -- 的任务中子体的位置时。这促使我们提出一个非常简单的特点转变,以缓解这一问题,被称作任务中心项目消除(TCCPR) 。TCCPR直接应用于特定任务中的所有图像特征,目的是消除任务中心点方向沿线的特征。虽然无法准确地从有限数据中获取准确的任务百分数,但我们估计它使用的基础特征与支助特征之一相似。我们的方法有效地防止了与任务中子过于接近的特征。对来自不同领域的十套数据集的广泛实验表明,TCPR可以可靠地改进不同特征提取器、培训算法和数据集的分类准确性。代码已在 https://BRK/BRIKK/TC 提供。