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Found 3 papers, 3 papers with code
Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip Connections
In this paper, we investigate convolutional denoising auto-encoders to show that unsupervised pre-training can still improve the performance of high-level image related tasks such as image classification and semantic segmentation.
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
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
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
We propose to symmetrically link convolutional and de-convolutional layers with skip-layer connections, with which the training converges much faster and attains a higher-quality local optimum.
Ranked #37 on
Image Super-Resolution
on BSD100 - 4x upscaling
