Denoising is the task of removing noise from an image.
( Image credit: Beyond a Gaussian Denoiser )
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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 #9 on Question Answering on SQuAD1.1 dev (F1 metric)
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.
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption.
Ranked #2 on Color Image Denoising on Kodak24 sigma30
Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers.
Ranked #2 on Grayscale Image Denoising on BSD200 sigma10
Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e. g., deblurring).
Ranked #1 on Color Image Denoising on BSD68 sigma35