2020/11/12 by LI Cheng-shuai, Li, Chengshuai, Shuai Han +3
Computer Science · Earth and Planetary Sciences · Physics and Astronomy · #Advanced Data Compression Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Model Reduction and Neural Networks #Seismic Imaging and Inversion Techniques
paper · pdf · doi:10.48550/arxiv.2011.06246
openalex publication_date 2020/11/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.