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title: Literature Review

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# Literature Review

## Uncertainity Quantification in Deep Learning

[A review of uncertainty quantification in deep learning: Techniques, applications and challenges](https://arxiv.org/pdf/2107.03342.pdf)
### Key Takeaways
- Five sources of uncertainity:
   1. Variability in Real World Situations
   2. Error and Noise in Measurement Systems
   3. Errors in the Model Structure
   4. Errors in the Training Procedure
   5. Errors Caused by Unknown Data
- Uncertainity Estimation Methods
    - Single deterministic methods
    - Bayesian methods
    - Ensemble Methods
    - Test-time augmentation methods
    - ![](https://i.imgur.com/L34PiJ0.jpg) 

  
