Implementation of an Autoencoder neural network for image reconstruction and dimensionality reduction using TensorFlow/Keras, including training, evaluation, and visualization of reconstructed outputs.
This repository contains the implementation of a Deep Autoencoder for image reconstruction and feature compression.
The project is implemented in Jupyter Notebook (Lab08_Autoencoder.ipynb) using TensorFlow / Keras.
Autoencoders are unsupervised neural networks that learn to efficiently compress and reconstruct data. This project demonstrates how an autoencoder can learn meaningful latent representations of images and then reconstruct them with minimal loss.
- Implementation of a Deep Autoencoder
- Image normalization and preprocessing
- Encoder → Bottleneck (latent space) → Decoder pipeline
- Unsupervised learning (no labels required)
- Visualization of:
- Original images
- Reconstructed images
- Reconstruction loss
- Demonstrates dimensionality reduction and feature learning
The notebook follows these main steps:
- Load and preprocess image dataset
- Normalize images to [0, 1]
- Build encoder network:
- Dense / Conv layers
- Bottleneck (latent vector)
- Build decoder network:
- Reverse of encoder
- Reconstruct image from latent vector
- Compile autoencoder using MSE / Binary Crossentropy loss
- Train the model on input images
- Visualize reconstruction results
- Evaluate reconstruction error
🛠️ Technologies Used
1.Python
2.TensorFlow / Keras
3.NumPy
4.Matplotlib
5.OpenCV / PIL
6.Scikit-learn
7.Jupyter Notebook
📈 Applications of This Model
1.Image denoising
2.Compression
3.Anomaly detection
4.Dimensionality reduction
5.Feature extraction
🔮 Future Improvements
1.Add Convolutional Autoencoder (CAE)
2.Implement Denoising Autoencoder
3.Add Variational Autoencoder (VAE)
4.Use for anomaly detection
5.Add latent space visualization (t-SNE / PCA)
👨💻 Author
Siddhi Hon
Deep Learning | AI Enthusiast