Optimizing Few-Shot Learning Based on Variational Autoencoders

Abstract

Despite the importance of few-shot learning, the lack of labeled training data in the real world makes it extremely challenging for existing machine learning methods because this limited dataset does not well represent the data variance. In this research, we suggest employing a generative approach using variational autoencoders (VAEs), which can be used specifically to optimize few-shot learning tasks by generating new samples with more intra-class variations on the Labeled Faces in the Wild (LFW) dataset. The purpose of our research is to increase the size of the training dataset using various methods to improve the accuracy and robustness of the few-shot face recognition. Specifically, we employ the VAE generator to increase the size of the training dataset, including the basic and the novel sets while utilizing transfer learning as the backend. Based on extensive experimental research, we analyze various data augmentation methods to observe how each method affects the accuracy of face recognition. The face generation method based on VAEs with perceptual loss can effectively improve the recognition accuracy rate to 96.47% using both the base and the novel sets.

Top-down generation of low-resolution representations improves visual perception and imagination

“…Such geometry-respecting representations are also observed in high-level brain areas such as prefrontal cortex and hippocampus [40, 41]. Developing this geometry-respecting representation is essential for semi-supervised learning [42, 43], few-shot learning [44], and data augmentation via imagination [44, 45]. Using VAE as a model of the visual system, we will study the computational advantage for low-resolution top-down generation ( Figure 1d ), and investigate the inspiration of this finding to advance the technique of sketch generation ( Figure 1e, f ).…”

“…Such geometry-respecting representations are also observed in high-level brain areas such as prefrontal cortex and hippocampus [40, 41]. Developing this geometry-respecting representation is essential for semi-supervised learning [42, 43], few-shot learning [44], and data augmentation via imagination [44, 45].…”

Unsupervised Few Shot Key Frame Extraction for Cow Teat Videos

“…Hong et al [37] utilized reinforcement learning for training an attention agent to generate discriminative representation in few-shot learning. Wei and Mahmood [38] optimized few-shot learning tasks by generating new samples using variational autoencoders on face recognition. However, current few-shot models are mostly supervised and rely on labeled examples.…”