Despite recent advances, the remaining bottlenecks in deep generative models are necessity of extensive training and difficulties with generalization from small number of training examples. Both problems may be addressed by conditional generative models that are trained to adapt the generative distribution to additional input data. So far this idea was explored only under certain limitations such as restricting the input data to be a single object or multiple objects representing the same concept. In this work we develop a new class of deep generative model called generative matching networks which is inspired by the recently proposed matching networks for one-shot learning in discriminative tasks and the ideas from meta-learning. By conditioning on the additional input dataset, generative matching networks may instantly learn new concepts that were not available during the training but conform to a similar generative process, without explicit limitations on the number of additional input objects or the number of concepts they represent. Our experiments on the Omniglot dataset demonstrate that generative matching networks can significantly improve predictive performance on the fly as more additional data is available to the model and also adapt the latent space which is beneficial in the context of feature extraction.

我们开发了一种基于“匹配网络”的新生成模型Generative Matching Network，该模型通过在额外输入数据集的条件下学习新的概念，无需使用广泛推理过程进行训练或适应，且在可用的更多附加数据的情况下显着提高了预测性能，优于现有的最先进条件生成模型。

用生成匹配网络实现生成模型的快速适应