We study the few-shot learning (FSL) problem, where a model learns to recognize new objects with extremely few labeled training data per category. Most of previous FSL approaches resort to the meta-learning paradigm, where the model accumulates inductive bias through learning many training tasks so as to solve a new unseen few-shot task. In contrast, we propose a simple semi-supervised FSL approach to exploit unlabeled data accompanying the few-shot task for improving few-shot performance. (i) Firstly, we propose a Dependency Maximization method based on the Hilbert-Schmidt norm of the cross-covariance operator, which maximizes the statistical dependency between the embedded features of those unlabeled data and their label predictions, together with the supervised loss over the support set. We then use the obtained model to infer the pseudo-labels of the unlabeled data. (ii) Furthermore, we propose an Instance Discriminant Analysis to evaluate the credibility of each pseudo-labeled example and select the most faithful ones into an augmented support set to retrain the model as in the first step. We iterate the above process until the pseudo-labels of the unlabeled set become stable. Our experiments demonstrate that the proposed method outperforms previous state-of-the-art methods on four widely used few-shot classification benchmarks, including mini-ImageNet, tiered-ImageNet, CUB, CIFARFS, as well as the standard few-shot semantic segmentation benchmark PASCAL-5\(^{i}\).

我们研究了少样本学习 (FSL) 问题，其中模型学习识别每个类别的标记训练数据极少的新对象。大多数以前的 FSL 方法都求助于元学习范式，其中模型通过学习许多训练任务来积累归纳偏差，以解决一个新的看不见的小样本任务。相比之下，我们提出了一种简单的半监督 FSL 方法来利用伴随少镜头任务的未标记数据来提高少镜头性能。(i) 首先，我们提出了一个依赖最大化基于交叉协方差算子的希尔伯特-施密特范数的方法，该方法最大化了那些未标记数据的嵌入特征与其标签预测之间的统计依赖性，以及支持集上的监督损失。然后我们使用获得的模型来推断未标记数据的伪标签。(ii) 此外，我们提出了实例判别分析评估每个伪标记示例的可信度，并选择最忠实的示例到增强的支持集中，以像第一步一样重新训练模型。我们重复上述过程，直到未标记集合的伪标签变得稳定。我们的实验表明，所提出的方法在四个广泛使用的小样本分类基准上优于以前的最新方法，包括mini -ImageNet、分层-ImageNet、CUB、CIFARFS，以及标准的小样本语义分割基准 PASCAL-5 \(^{i}\)。