Denoising is the task of removing noise from an image.
( Image credit: Beyond a Gaussian Denoiser )
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This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks.
We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.
Ranked #2 on Text Summarization on X-Sum
We show results for extractive and human baselines to demonstrate a large abstractive gap in performance.
In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning.
Recent advances in neural-network based generative modeling of speech has shown great potential for speech coding.
In this paper, we propose to use a 3D body mesh recovery module to disentangle the pose and shape, which can not only model the joint location and rotation but also characterize the personalized body shape.