Adaptive Anchor Label Propagation for Transductive Few-Shot Learning
Michalis Lazarou Yannis Avrithis Guangyu Ren Tania Stathaki
Few-shot learning addresses the issue of classifying images using limited
labeled data. Exploiting unlabeled data through the use of transductive
inference methods such as label propagation has been shown to improve the
performance of few-shot learning significantly. Label propagation infers
pseudo-labels for unlabeled data by utilizing a constructed graph that exploits
the underlying manifold structure of the data. However, a limitation of the
existing label propagation approaches is that the positions of all data points
are fixed and might be sub-optimal so that the algorithm is not as effective as
possible. In this work, we propose a novel algorithm that adapts the feature
embeddings of the labeled data by minimizing a differentiable loss function
optimizing their positions in the manifold in the process. Our novel algorithm,
Adaptive Anchor Label Propagation}, outperforms the standard label propagation
algorithm by as much as 7% and 2% in the 1-shot and 5-shot settings
respectively. We provide experimental results highlighting the merits of our
algorithm on four widely used few-shot benchmark datasets, namely miniImageNet,
tieredImageNet, CUB and CIFAR-FS and two commonly used backbones, ResNet12 and
WideResNet-28-10. The source code can be found at
https://github.com/MichalisLazarou/A2LP.