Fast Flow Reconstruction via Robust Invertible n × n Convolution

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.

OTAdapt: Optimal Transport-based Approach For Unsupervised Domain Adaptation

“…Deep learning-based image recognition studies have been recently achieving very accurate performance in visual applications, e.g. image classification [1], [2], [3], face recognition, [4], [5], [6], [7], [8], image synthesis [9], [10], [11], [12], [13], [14], action recognition [15], [16], semantic segmentation [17], [18]. However, these methods assume the testing images from the same distribution as the training images, therefore, these deep learning-based models are likely to fail when performing in real data in the new domains.…”