Results 31 to 40 of about 259,715 (355)
Multilingual Denoising Pre-training for Neural Machine Translation [PDF]
Transactions of the Association for Computational Linguistics, 2020 This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks.
Yinhan Liu +7 more
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Denoising Diffusion Samplers [PDF]
International Conference on Learning Representations, 2023 Denoising diffusion models are a popular class of generative models providing state-of-the-art results in many domains. One adds gradually noise to data using a diffusion to transform the data distribution into a Gaussian distribution.
Francisco Vargas +2 more
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IEEE Access, 2022 Traditional denoising methods for seismic exploration data design a corresponding mathematical denoising model batch according to the different properties of different random noises, which is a tedious and time-consuming process.
Liang Guo +5 more
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Denoising Diffusion Bridge Models [PDF]
International Conference on Learning Representations, 2023 Diffusion models are powerful generative models that map noise to data using stochastic processes. However, for many applications such as image editing, the model input comes from a distribution that is not random noise.
Linqi Zhou +3 more
semanticscholar +1 more source
Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm
Technologies, 2023 Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details.
Rusul Sabah Jebur +4 more
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Denoising an Image by Denoising Its Curvature Image [PDF]
SIAM Journal on Imaging Sciences, 2014 The first author acknowledges partial support by European Research Council, Starting Grant ref. 306337, and/nby Spanish grants AACC, ref. TIN2011-15954-E, and Plan Nacional, ref. TIN2012-38112. The second author was supported in part by NSF-DMS #0915219.
Marcelo Bertalmío, Stacey Levine
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ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models [PDF]
IEEE International Conference on Computer Vision, 2021 Denoising diffusion probabilistic models (DDPM) have shown remarkable performance in unconditional image generation. However, due to the stochasticity of the generative process in DDPM, it is challenging to generate images with the desired semantics.
Jooyoung Choi +4 more
semanticscholar +1 more source
Auto-Denoising for EEG Signals Using Generative Adversarial Network
Sensors, 2022 The brain–computer interface (BCI) has many applications in various fields. In EEG-based research, an essential step is signal denoising. In this paper, a generative adversarial network (GAN)-based denoising method is proposed to denoise the multichannel
Yang An, Hak Keung Lam, Sai Ho Ling
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GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks [PDF]
ACM Transactions on Graphics, 2022 In this article, we present GCN-Denoiser, a novel feature-preserving mesh denoising method based on graph convolutional networks ( GCNs ). Unlike previous learning-based mesh denoising methods that exploit handcrafted or voxel-based representations for feature learning, our method explores ...
Shen, Yuefan +7 more
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Zero-Shot Noise2Noise: Efficient Image Denoising without any Data [PDF]
Computer Vision and Pattern Recognition, 2023 Recently, self-supervised neural networks have shown excellent image denoising performance. How-ever, current dataset free methods are either computationally expensive, require a noise model, or have inad-equate image quality. In this work we show that a
Youssef Mansour, Reinhard Heckel
semanticscholar +1 more source