Multi-Pretext Attention Network for Few-shot Learning with Self-supervision
Few-shot learning is an interesting and challenging study, which enables machines to learn from few samples like humans. Existing studies rarely exploit auxiliary information from large amount of unlabeled data. Self-supervised learning is emerged as an efficient method to utilize unlabeled data. Existing self-supervised learning methods always rely on the combination of geometric transformations for the single sample by augmentation, while seriously neglect the endogenous correlation information among different samples that is the same important for the task. In this work, we propose a Graph-driven Clustering (GC), a novel augmentation-free method for self-supervised learning, which does not rely on any auxiliary sample and utilizes the endogenous correlation information among input samples. Besides, we propose Multi-pretext Attention Network (MAN), which exploits a specific attention mechanism to combine the traditional augmentation-relied methods and our GC, adaptively learning their optimized weights to improve the performance and enabling the feature extractor to obtain more universal representations. We evaluate our MAN extensively on miniImageNet and tieredImageNet datasets and the results demonstrate that the proposed method outperforms the state-of-the-art (SOTA) relevant methods.
Code (2)
Tasks
ClusteringFew-Shot LearningSelf-Supervised LearningSimilar Papers 제목 키워드 기반
Improving out-of-distribution generalization via multi-task self-supervised pretraining
Self-supervised feature representations have been shown to be useful for supervised classification, few-shot learning, and adversarial robustness. We show that features obtained using self-supervised learning are compara…
Adversarial RobustnessDomain GeneralizationFew-Shot LearningMulti-Task Learning+2Self-supervision of Feature Transformation for Further Improving Supervised Learning
Self-supervised learning, which benefits from automatically constructing labels through pre-designed pretext task, has recently been applied for strengthen supervised learning. Since previous self-supervised pretext task…
Self-Supervised LearningTailoring Self-Supervision for Supervised Learning
Recently, it is shown that deploying a proper self-supervision is a prospective way to enhance the performance of supervised learning. Yet, the benefits of self-supervision are not fully exploited as previous pretext tas…
Adversarial RobustnessData AugmentationImage Classificationimbalanced classification+2Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation
In this paper, we mainly focus on the problem of how to learn additional feature representations for few-shot image classification through pretext tasks (e.g., rotation or color permutation and so on). This additional kn…
Few-Shot Image ClassificationFew-Shot Learningimage-classificationImage ClassificationDigging into Uncertainty in Self-supervised Multi-view Stereo
Self-supervised Multi-view stereo (MVS) with a pretext task of image reconstruction has achieved significant progress recently. However, previous methods are built upon intuitions, lacking comprehensive explanations abou…
Image ReconstructionSelf-Supervised Learning