# Lecture - Theories of Deep Learning MT25, XII, The scattering transform and into auto-encoders

> Source: https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/lectures/auto-encoders/ · Updated: 2025-11-19 · Tags: uni, lecture

- [Course - Theories of Deep Learning MT25](https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/)

- Scattering transform
	- Given a task, consider all the invariants you want to model and then design the activation functions to remove these. The scattering transform does this for translation
- Autoencoders
	- Principal component analysis can be viewed as an autoencoder
	- Autoencoders can be viewed as an extension of PCA by allowing transformations that aren't just low dimensional projections
	- $k$-sparse autoencoders: 
- Adversarial examples
	- How do the decision region diagrams change when you have an additional "don't know" class?

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