https://github.com/amanpriyanshu/denoising-autoencoder
The Denoising Autoencoder is an extension of the autoencoder. Just as a standard autoencoder, it’s composed of an encoder, that compresses the data into the latent code, extracting the most relevant features, and a decoder, which decompress it and reconstructs the original input. There is only a slight modification: the Denoising Autoencoder takes a noisy image as input and the target for the output layer is the original input without noise.
https://github.com/amanpriyanshu/denoising-autoencoder
Last synced: 6 months ago
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The Denoising Autoencoder is an extension of the autoencoder. Just as a standard autoencoder, it’s composed of an encoder, that compresses the data into the latent code, extracting the most relevant features, and a decoder, which decompress it and reconstructs the original input. There is only a slight modification: the Denoising Autoencoder takes a noisy image as input and the target for the output layer is the original input without noise.
- Host: GitHub
- URL: https://github.com/amanpriyanshu/denoising-autoencoder
- Owner: AmanPriyanshu
- Created: 2021-12-11T05:13:58.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2021-12-11T09:16:04.000Z (almost 4 years ago)
- Last Synced: 2025-02-08T17:24:01.889Z (8 months ago)
- Language: Python
- Size: 98.5 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Denoising-AutoEncoder
The Denoising Autoencoder is an extension of the autoencoder. Just as a standard autoencoder, it’s composed of an encoder, that compresses the data into the latent code, extracting the most relevant features, and a decoder, which decompress it and reconstructs the original input. There is only a slight modification: the Denoising Autoencoder takes a noisy image as input and the target for the output layer is the original input without noise.
[Reference](https://ai.plainenglish.io/denoising-autoencoder-in-pytorch-on-mnist-dataset-a76b8824e57e)