Answers.Body: <p>Data augmentation typically refers to the creation of new (training) data/instances (e.g. images) by e.g. modifying existing training/instances data in order to <strong>avoid over-fitting</strong> and <strong>improve the performance</strong> (e.g. generalization, precision, recall, etc.) of the model.</p>
  <p>There are other techniques that attempt to mitigate over-fitting, for example, regularization techniques (like dropout), but data augmentation changes the data that you use rather than how you use it, or how you define and train/test a model.</p>
  <p>When you use data augmentation, you're assuming that more information can be extracted from the original dataset. For example, if you augment your training dataset by rotating existing images of objects, then you assume that this can be beneficial for your task (i.e. you expect your model to encounter rotated versions of the same object).</p>
  <p>Typical scenarios where you may need data augmentation are when you have small training or imbalanced datasets.</p>
  <p>Padding is not a data augmentation technique because you don't do it to avoid overfitting. Padding is a <a href="https://ai.stackexchange.com/a/17119/2444">data preprocessing</a> technique because you do it in order for your model to even be able to process your data.</p>
  <p>You could read <a href="https://link.springer.com/content/pdf/10.1186/s40537-019-0197-0.pdf" rel="nofollow noreferrer">A survey on Image Data Augmentation for Deep Learning</a> (by Shorten &amp; Khoshgoftaar, 2019, Journal of Big Data), which I have only skimmed through, but it looks like a good paper.</p>
  
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Answers.CreationDate: 2022-01-02T23:38:45.330Z
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Body: <p>Data augmentation is useful in training. But, I am not sure when can a modification applied to data can be called data augmentation.</p>
  <p>Suppose a technique is applied to the instances of a dataset and the size of the dataset does not increase but the instances change slightly, then can I call it a data augmentation technique?</p>
  <p>For example, say padding, never increases the size of the dataset but adds some data to possibly every existing training sample. In other cases, such as rotating the images with an angle, then the number of instances increases in the dataset.</p>
  
Tags: <definitions><data-augmentation>
Title: What exactly is data augmentation?
UserDisplayName: hanugm
ThreadText: # What exactly is data augmentation?
  
  (Asked by: username_0 on 2022-01-02)
  
  Data augmentation is useful in training. But, I am not sure when can a modification applied to data can be called data augmentation.
  
  Suppose a technique is applied to the instances of a dataset and the size of the dataset does not increase but the instances change slightly, then can I call it a data augmentation technique?
  
  For example, say padding, never increases the size of the dataset but adds some data to possibly every existing training sample. In other cases, such as rotating the images with an angle, then the number of instances increases in the dataset.
  
  **Tags:** definitions, data-augmentation
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2022-01-02. Score: 1 | Accepted answer
  
  Data augmentation typically refers to the creation of new (training) data/instances (e.g. images) by e.g. modifying existing training/instances data in order to **avoid over-fitting** and **improve the performance** (e.g. generalization, precision, recall, etc.) of the model.
  
  There are other techniques that attempt to mitigate over-fitting, for example, regularization techniques (like dropout), but data augmentation changes the data that you use rather than how you use it, or how you define and train/test a model.
  
  When you use data augmentation, you're assuming that more information can be extracted from the original dataset. For example, if you augment your training dataset by rotating existing images of objects, then you assume that this can be beneficial for your task (i.e. you expect your model to encounter rotated versions of the same object).
  
  Typical scenarios where you may need data augmentation are when you have small training or imbalanced datasets.
  
  Padding is not a data augmentation technique because you don't do it to avoid overfitting. Padding is a [data preprocessing](https://ai.stackexchange.com/a/17119/2444) technique because you do it in order for your model to even be able to process your data.
  
  You could read [A survey on Image Data Augmentation for Deep Learning](https://link.springer.com/content/pdf/10.1186/s40537-019-0197-0.pdf) (by Shorten & Khoshgoftaar, 2019, Journal of Big Data), which I have only skimmed through, but it looks like a good paper.
  
  
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