A Multitask Latent Feature Augmentation Method for Few-Shot Learning.

Few-shot learning (FSL) aims to learn novel concepts quickly from a few novel labeled samples with the transferable knowledge learned from base dataset. The existing FSL methods usually treat each sample as a single feature point in embedding space and classify through one single comparison task. However, the few-shot single feature points on the novel meta-testing episode are still vulnerable to noise easily although with the good transferable knowledge, because the novel categories are never seen on base dataset. Besides, the existing FSL models are trained by only one single comparison task and ignore that different semantic feature maps have different weights on different comparison objects and tasks, which cannot take full advantage of the valuable information from different multiple comparison tasks and objects to make the latent features (LFs) more robust based on only few-shot samples. In this article, we propose a novel multitask LF augmentation (MTLFA) framework to learn the meta-knowledge of generalizing key intraclass and distinguishable interclass sample features from only few-shot samples through an LF augmentation (LFA) module and a multitask (MT) framework. Our MTLFA treats the support features as sampling from the class-specific LF distribution, enhancing the diversity of support features and reducing the impact of noise based on few-shot support samples. Furthermore, an MT framework is introduced to obtain more valuable comparison-task-related and episode-related comparison information from multiple different comparison tasks in which different semantic feature maps have different weights, adjusting the prior LFs and generating the more robust and effective episode-related classifier. Besides, we analyze the feasibility and effectiveness of MTLFA from theoretical views based on the Hoeffding's inequality and the Chernoff's bounding method. Extensive experiments conducted on three benchmark datasets demonstrate that the MTLFA achieves the state-of-the-art performance in FSL. The experimental results verify our theoretical analysis and the effectiveness and robustness of MTLFA framework in FSL.