Inspired by these works, we propose a deep generative model that aims at triplet augmentation for k-shot learning.
during the meta-training phase, the recognition network takes O as input and learns the variational parameters µ θ and σ θ of the variational posterior q θ (z|O), where the latent semantics z is assumed to follow a Gaussian distribution.