https://github.com/chaganti-reddy/variational-autoencoder
Simple implementation of Variational Autoencoder on MNIST with keras number Dataset
https://github.com/chaganti-reddy/variational-autoencoder
cnn-keras encoder-decoder noise-reduction variational-autoencoder
Last synced: about 1 year ago
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Simple implementation of Variational Autoencoder on MNIST with keras number Dataset
- Host: GitHub
- URL: https://github.com/chaganti-reddy/variational-autoencoder
- Owner: Chaganti-Reddy
- License: mit
- Created: 2024-04-04T04:38:32.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-09T03:38:39.000Z (almost 2 years ago)
- Last Synced: 2024-09-09T04:54:50.584Z (almost 2 years ago)
- Topics: cnn-keras, encoder-decoder, noise-reduction, variational-autoencoder
- Language: Jupyter Notebook
- Homepage:
- Size: 5.42 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Variational Autoencoder
### Autoencoder
- neural network with unsupervised machine-learning algorithm apply back-prop to set target value to the input
- auto-encoder prefers over PCA because it can learn non-linear transformations with non-linear activation functions. more efficient to learn several layer with auto-encoder then one huge transformation with PCA.
### Autoencoder Applications
- Image coloring (Black-white images -> colored)
- Feature variation (Extract required feature)
- Dimensionality Reduction
- Denosing image (Remove Noise)
- Remove watermark
### Autoencoder Architecture
- Encoder : part of NN compress the input into latent space representation
- code : part of NN represents compressed input
- Decoder : Decode the encoded data to original dimension
### Properties of Autoencoder
- Data-specific: Autoencoders are only able to meaningfully compress data similar to what they have been trained on.
- Lossy: de-compressed output will be degrad compared to the original input
- Unsupervised: Autoencoders are considered an unsupervised learning technique since they don’t need explicit labels to train on. But to be more precise they are self-supervised because they generate their own labels from the training data.
### Types of Autoencoder
1. Denoising autoencoder.
2. Sparse Autoencoder.
3. Deep Autoencoder.
4. Contractive Autoencoder.
5. Undercomplete Autoencoder.
6. Convolutional Autoencoder.
7. Variational Autoencoder.
# convolutional Variational Autoencoder (Mnist)
### Model architecture
### Generated Numbers from latent space
### Encoded Dimension of Latent space from 2 to 10
### Remove Noise and Prediction
| | **Latent space 2** |
| :--------------------: | :-----------------------------------------------------------: |
| **Original Numbers** | 
|
| **Numbers prediction** | 
|
| **Noisy Numbers** | 
|
| **Remove Noise** | 
|
| | **Latent space 3** |
| :--------------------: | :-----------------------------------------------------------: |
| **Original Numbers** | 
|
| **Numbers prediction** | 
|
| **Noisy Numbers** | 
|
| **Remove Noise** | 
|
| | **Latent space 4** |
| :--------------------: | :-----------------------------------------------------------: |
| **Original Numbers** | 
|
| **Numbers prediction** | 
|
| **Noisy Numbers** | 
|
| **Remove Noise** | 
|
| | **Latent space 5** |
| :--------------------: | :-----------------------------------------------------------: |
| **Original Numbers** | 
|
| **Numbers prediction** | 
|
| **Noisy Numbers** | 
|
| **Remove Noise** | 
|
| | **Latent space 6** |
| :--------------------: | :-----------------------------------------------------------: |
| **Original Numbers** | 
|
| **Numbers prediction** | 
|
| **Noisy Numbers** | 
|
| **Remove Noise** | 
|
| | **Latent space 7** |
| :--------------------: | :-----------------------------------------------------------: |
| **Original Numbers** | 
|
| **Numbers prediction** | 
|
| **Noisy Numbers** | 
|
| **Remove Noise** | 
|
| | **Latent space 8** |
| :--------------------: | :-----------------------------------------------------------: |
| **Original Numbers** | 
|
| **Numbers prediction** | 
|
| **Noisy Numbers** | 
|
| **Remove Noise** | 
|
| | **Latent space 9** |
| :--------------------: | :-----------------------------------------------------------: |
| **Original Numbers** | 
|
| **Numbers prediction** | 
|
| **Noisy Numbers** | 
|
| **Remove Noise** | 
|
| | **Latent space 10** |
| :--------------------: | :------------------------------------------------------------: |
| **Original Numbers** | 
|
| **Numbers prediction** | 
|
| **Noisy Numbers** | 
|
| **Remove Noise** | 
|