该论文提出了一种通过模态特定对齐变分自编码器学习图像特征和类别嵌入共享潜在空间的方法，以解决通用零样本和少样本学习问题。通过对图像和侧信息的分布进行对齐，生成包含未见过类别关键多模态信息的潜在特征。在CUB、SUN、AWA1和AWA2等基准数据集上，该方法在通用零样本和少样本学习中取得了新的最佳结果，并在ImageNet的大规模设置中显示出良好的泛化能力。
PDF:Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
code:implement by pytorch
摘要
Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space. As labeled images are expensive, one direction is to augment the dataset by generating either images or image features. However, the former misses fine-grained details and the latter requires learning a mapping associated with class embeddings. In this work,we take feature generation one step further and propose a model where a shared latent space of image features and class embeddings is learned by modality-specific aligned variational autoencoders. This leaves us with the required discriminative information about the image and classes in the latent features, on which we train
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