This document discusses simple autoencoders. It defines autoencoders and describes their basic architecture and training process. It outlines eight types of autoencoders and provides examples of their applications, such as denoising images, dimensionality reduction, and anomaly detection. While autoencoders are not as efficient as generative adversarial networks for image reconstruction, they are still useful for tasks like dimensionality reduction and anomaly detection.

1. SIMPLE AUTOENCODERS
GROUP MEMBERS:
Aqsa Ijaz
MCS071201037
Anam Safdar
MCS071201056

3. AUTOENCODERS

4. AUTOENCODERS

5. ARCHITECTURE OF
AUTOENCODERS

6. AUTOENCODERS

7. AUTOENCODERS TRAINING

8. TYPES OF AUTOENCODERS
There are, basically, 8 types of autoencoders:
1.Denoising Autoencoder
2.Sparse Autoencoder.
3.Deep Autoencoder.
4.Contractive Autoencoder.
5.Stack Autoencoders
6.Undercomplete Autoencoder.
7.Convolutional Autoencoder.
8.Variational Autoencoder.

9. APPLICATIONS OF AUTOENCODERS
 DENOISING AUTOENCODERS:

10. APPLICATIONS OF AUTOENCODERS
 IMAGE COLOURISATION

11. APPLICATIONS OF AUTOENCODERS
 Remove watermarks

12. APPLICATIONS OF AUTOENCODERS
 Dimensionality Reduction
ORIGINA
L IMAGE
RE-
CONSTRUCTIV
E IMAGE

13. APPLICATIONS OF AUTOENCODERS
 Regenerate image

14. APPLICATIONS OF AUTOENCODERS
 Self driving cars

15. APPLICATIONS OF AUTOENCODERS
 Neural inpainting

16. APPLICATIONS OF AUTOENCODERS
 Anomaly detection
 Sequence to sequence
prediction
 Recommendation systems…

17. DISADVANTAGE
Autoencoders are not that efficient compared to Generative
Adversarial Networks in reconstructing an image. As the
complexity of the images increase, autoencoders struggle to
keep up and images start to get blurry.

18. CONCLUSION
Autoencoders are a very useful dimensionality
reduction technique. They are very popular as a
teaching material in introductory deep learning
courses, most likely due to their simplicity.
Even though autoencoders might struggle to keep
up with GANs, they are highly efficient in certain
tasks such as anomaly detection and others. This is
still a burgeoning field of neural network.

19. THANK YOU