2019/10/01 by Spyros Gidaris, Andrei Bursuc, Nikos Komodakis +2 · 1 citation
Computer Science · #Domain Adaptation and Few-Shot Learning #Multimodal Machine Learning Applications #Advanced Neural Network Applications
paper · doi:10.1109/iccv.2019.00815
openalex publication_date 2019/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data. Few-shot learning aims for optimization methods and models that can learn efficiently to recognize patterns in the low data regime. Self-supervised learning focuses instead on unlabeled data and looks into it for the supervisory signal to feed high capacity deep neural networks. In this work we exploit the complementarity of these two domains and propose an approach for improving few-shot learning through self-supervision. We use self-supervision as an auxiliary task in a few-shot learning pipeline, enabling feature extractors to learn richer and more transferable visual representations while still using few annotated samples. Through self-supervision, our approach can be naturally extended towards using diverse unlabeled data from other datasets in the few-shot setting. We report consistent improvements across an array of architectures, datasets and self-supervision techniques. We provide the implementation code at: https://github.com/valeoai/BF3S.