Fast Flow Reconstruction via Robust Invertible n × n Convolution

Thanh-Dat Truong, Chi Nhan Duong, Minh-Triet Tran, Ngan Le and Khoa Luu
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Thanh-Dat Truong: Computer Vision and Image Understanding Lab, Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72501, USA
Chi Nhan Duong: Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 2V4, Canada
Minh-Triet Tran: Faculty of Information Technology, University of Science, VNU-HCM, Ho Chi Minh 721337, Vietnam
Ngan Le: Computer Vision and Image Understanding Lab, Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72501, USA
Khoa Luu: Computer Vision and Image Understanding Lab, Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72501, USA

Future Internet, 2021, vol. 13, issue 7, 1-12

Abstract: Flow-based generative models have recently become one of the most efficient approaches to model data generation. Indeed, they are constructed with a sequence of invertible and tractable transformations. Glow first introduced a simple type of generative flow using an invertible 1 × 1 convolution. However, the 1 × 1 convolution suffers from limited flexibility compared to the standard convolutions. In this paper, we propose a novel invertible n × n convolution approach that overcomes the limitations of the invertible 1 × 1 convolution. In addition, our proposed network is not only tractable and invertible but also uses fewer parameters than standard convolutions. The experiments on CIFAR-10, ImageNet and Celeb-HQ datasets, have shown that our invertible n × n convolution helps to improve the performance of generative models significantly.

Keywords: flow-based generative model; invertible n × n convolution; invertible and tractable transformations (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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