https://github.com/arpan132002/deep-cnn-autoencoder

Welcome to the Deep CNN Autoencoder project! This repository contains the code and resources for building a deep convolutional neural network (CNN) autoencoder.
https://github.com/arpan132002/deep-cnn-autoencoder

architecture autoencoder cnn deep-learning

Last synced: about 1 month ago
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Welcome to the Deep CNN Autoencoder project! This repository contains the code and resources for building a deep convolutional neural network (CNN) autoencoder.

• Host: GitHub
• URL: https://github.com/arpan132002/deep-cnn-autoencoder
• Owner: arpan132002
• Created: 2024-08-05T11:25:39.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-08-07T05:00:10.000Z (5 months ago)
• Last Synced: 2024-08-08T08:29:39.461Z (5 months ago)
• Topics: architecture, autoencoder, cnn, deep-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 64.5 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Deep CNN Autoencoder - Image Compression - Denoising Image

Welcome to the **Deep CNN Autoencoder** project! This repository contains the code and resources for building a deep convolutional neural network (CNN) autoencoder. The autoencoder is designed to learn efficient representations of data by encoding input data into a lower-dimensional space and then reconstructing it back to its original form.

# Project Information

An autoencoder is an unsupervised learning technique for neural networks that learns efficient data representations (encoding) by training the network to ignore signal “noise.” Autoencoders can be used for image denoising, image compression, and, in some cases, even generation of image data.

## Flow of Autoencoder

Input Image -> Encoder -> Compressed Representation -> Decoder -> Reconstruct Input Image

## Features

- **Deep CNN Architecture**: Utilizes multiple convolutional layers for feature extraction and encoding.

- **Efficient Dimensionality Reduction**: Learns compressed representations of the input data.

- **Reconstruction Capability**: Reconstructs input data from its encoded representation.

- **Versatile Application**: Can be applied to various domains such as image compression, noise reduction, and anomaly detection.

### Requirements

- Python 3.x

- TensorFlow / Keras

- NumPy

- Matplotlib