Transductive few-shot learning algorithms have showed substantially superior performance over their inductive counterparts by leveraging the unlabeled queries. However, the vast majority of such methods are evaluated on perfectly class-balanced benchmarks. It has been shown that they undergo remarkable drop in performance under a more realistic, imbalanced setting. To this end, we propose a novel algorithm to address imbalanced transductive few-shot learning, named Adaptive Manifold. Our method exploits the underlying manifold of the labeled support examples and unlabeled queries by using manifold similarity to predict the class probability distribution per query. It is parameterized by one centroid per class as well as a set of graph-specific parameters that determine the manifold. All parameters are optimized through a loss function that can be tuned towards class-balanced or imbalanced distributions. The manifold similarity shows substantial improvement over Euclidean distance, especially in the 1-shot setting. Our algorithm outperforms or is on par with other state of the art methods in three benchmark datasets, namely miniImageNet, tieredImageNet and CUB, and three different backbones, namely ResNet-18, WideResNet-28-10 and DenseNet-121. In certain cases, our algorithm outperforms the previous state of the art by as much as 4.2%.

翻译：直推式小样本学习算法通过利用未标注查询样本，展现了相较于归纳式方法显著更优的性能。然而，绝大多数此类方法均在完美类平衡基准上评估。研究表明，在更现实的非平衡设置下，其性能出现显著下降。为此，我们提出一种名为自适应流形的新型算法，旨在解决非平衡直推式小样本学习问题。该方法利用流形相似性预测每个查询样本的类别概率分布，从而挖掘标注支持样本与未标注查询样本的潜在流形结构。算法由每类一个质心以及一组决定流形结构的图特定参数进行参数化。所有参数通过一个可针对类平衡或非平衡分布进行调整的损失函数进行优化。流形相似性相比欧氏距离展现出显著优势，尤其在单样本设置中。我们的算法在三个基准数据集（即miniImageNet、tieredImageNet和CUB）及三种不同主干网络（即ResNet-18、WideResNet-28-10和DenseNet-121）上，性能优于或持平于现有最先进方法。在某些情况下，该算法相比之前最先进方法性能提升高达4.2%。