论文标题
VCE：用于单发概括的变异转换器编码器
VCE: Variational Convertor-Encoder for One-Shot Generalization
论文作者
论文摘要
变性转换器编码器（VCE）将图像转换为各种样式；我们介绍了这种新颖的体系结构，以解决单发概括的问题及其转移到没有其他培训的情况下看不到的新任务。我们还提高了变异自动编码器（VAE）的性能，以使用我们提出的新算法（即较大的边距VAE（LMVAE））过滤这些模糊点。两个具有相同属性的样本是输入编码器的，然后需要一个转换器才能从编码器的嘈杂输出中处理其中一个。最后，噪声代表各种转换规则，用于转换新图像。结合并改善条件变化自动编码器（CVAE）和内省性vae的算法，我们提出了这个新框架旨在转换图形而不是生成图形。它用于一次性生成过程。在训练中不需要顺序推断算法。与最近的Omniglot数据集相比，结果表明，我们的模型会产生更现实和多样化的图像。
Variational Convertor-Encoder (VCE) converts an image to various styles; we present this novel architecture for the problem of one-shot generalization and its transfer to new tasks not seen before without additional training. We also improve the performance of variational auto-encoder (VAE) to filter those blurred points using a novel algorithm proposed by us, namely large margin VAE (LMVAE). Two samples with the same property are input to the encoder, and then a convertor is required to processes one of them from the noisy outputs of the encoder; finally, the noise represents a variety of transformation rules and is used to convert new images. The algorithm that combines and improves the condition variational auto-encoder (CVAE) and introspective VAE, we propose this new framework aim to transform graphics instead of generating them; it is used for the one-shot generative process. No sequential inference algorithmic is needed in training. Compared to recent Omniglot datasets, the results show that our model produces more realistic and diverse images.
