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Creators/Authors contains: "Truong, Thanh-Dat"

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  1. ; ; ; ; ; (, Neurocomputing)
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  2. ; ; ; ; ; (, 2022 IEEE International Conference on Image Processing (ICIP))
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  3. ; ; ; ; ; (, 2022 26th International Conference on Pattern Recognition (ICPR))
  4. ; ; ; ; ; (, IEEE Access)
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  5. ; ; ; ; ; ; (, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
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  6. ; ; ; ; (, Future Internet)
    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. 
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