# A Bayesian Data Augmentation Approach for Learning Deep Models

**Authors:** Toan Tran, Trung Pham, Gustavo Carneiro, Lyle Palmer, Ian Reid  
**arXiv:** [1710.10564](https://arxiv.org/abs/1710.10564)  
**Date:** 2017-10-29  
**Summary page:** https://www.summarizepaper.com/en/paper/1710.10564v1/

## Key points

- Data augmentation is important in training deep learning models
- Large annotated datasets are costly to acquire, store, and process
- Authors propose a Bayesian data augmentation approach as an alternative
- Current dominant data augmentation approach may not reliably generate new training samples
- Authors present a novel Bayesian formulation for data augmentation
- They introduce a theoretically sound algorithm called generalised Monte Carlo expectation maximisation
- Proposed method implemented using an extension of the Generative Adversarial Network (GAN)
- Results show better classification performance on datasets such as MNIST, CIFAR-10, and CIFAR-100 compared to current approaches
- Their approach outperforms similar GAN models in terms of classification accuracy
- This research contributes to advancing the field of deep learning model training.

## Summary

In the paper "A Bayesian Data Augmentation Approach for Learning Deep Models," authors Toan Tran, Trung Pham, Gustavo Carneiro, Lyle Palmer, and Ian Reid discuss the importance of data augmentation in training deep learning models. They highlight that a robust training process for these models relies on large annotated datasets, which can be costly to acquire, store, and process. As a result, they propose an alternative approach called data augmentation which involves automatically generating new annotated training samples. The current dominant data augmentation approach in the field assumes that new training samples can be obtained through random geometric or appearance transformations applied to annotated training samples. However, the authors argue that this assumption may not always hold true as it is unclear if this method reliably generates new training samples. To address this issue, the authors present a novel Bayesian formulation for data augmentation. They treat new annotated training points as missing variables and generate them based on the distribution learned from the existing training set. To facilitate learning with this approach they introduce a theoretically sound algorithm called generalised Monte Carlo expectation maximisation. The authors demonstrate one possible implementation of their proposed method using an extension of the Generative Adversarial Network (GAN). They compare their results with those obtained using the current dominant data augmentation approach mentioned earlier and show that their approach achieves better classification performance on datasets such as MNIST, CIFAR-10 and CIFAR-100. Additionally their results indicate that their approach outperforms similar GAN models in terms of classification accuracy. Overall this paper presents a promising Bayesian data augmentation approach for learning deep models. By addressing limitations associated with existing methods and demonstrating improved classification performance compared to current approaches and similar GAN models this research contributes to advancing the field of deep learning model training.