# CSE5519 Advances in Computer Vision (Topic F: 2025: Representation Learning)

## Can Generative Models Improve Self-Supervised Representation Learning?

[link to the paper](https://arxiv.org/pdf/2403.05966)

### Novelty in SSL with Generative Models

- Use generative models to generate synthetic data to train self-supervised representation learning models.
- Use generative augmentation to generate new data from the original data using a generative model. (with gaussian noise, or other data augmentation techniques)
- Using standard augmentation techniques like flipping, cropping, and color jittering with generative techniques can further improve the performance of the self-supervised representation learning models.

> [!TIP]
>
> This paper shows that using generative models to generate synthetic data can improve the performance of self-supervised representation learning models. The key seems to be the use of generative augmentation to generate new data from the original data using a generative model.
> 
> However, both representation learning and generative modeling have some hallucinations. I wonder will these kinds of hallucinations would be reinforced, and the bias in the generation model would propagate to the representation learning model in the process of generative augmentation?
