A Survey on Image Data Augmentation for Deep Learning: Review of Approaches and Recent Advances
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Data augmentation is a technique that can be used to increase the amount of data available for training deep learning models. By artificially creating new data points, we can train our models on more diverse data, which can lead to improved performance.
There are many ways to perform data augmentation, including photometrics methods such as translation, rotation, and shearing; as well as signal processing methods such as adding noise or change of color space. In this survey, we will review the most commonly used image augmentation techniques and their effectiveness on deep learning tasks.
What is Image Data Augmentation?
Image data augmentation is a technique that can be used to artificially increase the size of a training dataset by creating modified versions of images in the dataset. Training deep learning models on large datasets can be time-consuming and costly, so data augmentation can be used as a way to improve model performance without increasing the size of the training dataset. Data augmentation has been shown to be effective in a variety of tasks, including image classification, object detection, and semantic segmentation.
Why is Image Data Augmentation Important for Deep Learning?
Deep learning algorithms require large amounts of data in order to train effectively. Image data augmentation is a technique that can be used to generate additional training data by applying random transformations to existing images. This can help prevent overfitting and improve the generalizability of the model.
How does Image Data Augmentation Work?
Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Data augmentation is an important tool in deep learning because it can help to avoid overfitting, which is when a model performs well on training data but does not generalize well to new data.
Image data augmentation works by randomly modifying existing images in the training dataset. The modifications can be anything from simple changes like flipping or cropping an image, to more complex changes like adding noise or changing the color balance. The hope is that by artificially expanding the training dataset, the model will learn generalizable features that can be applied to new data.
There are a number of different techniques that can be used for image data augmentation, and the choice of technique will depend on the type of data you are working with and the desired application. In general, however, most approaches involve randomly selected modification operators that are applied to images in the training dataset.
What are the Benefits of Image Data Augmentation?
Image data augmentation is a method that can used to artificially increase the size of a training dataset by modifying existing images and adding new ones created from scratch. The modification could be done by applying random transformations, such as cropping, rotation, translation, shearing, zooming, and flipping. In addition to randomly applying these transformations, the degree of transformation can also be varied randomly. For example, the maximum amount of rotation could be increased from 10 degrees to 20 degrees. This would give the model more variety to learn from and help it generalize better to new data.
Applying random transformations to an image will usually result in an image that does not look natural. However, the model will not be trained on these images, so it does not matter if they look natural or not. The goal is simply to provide the model with more examples to learn from so that it can better generalize to new data.
There are several benefits of image data augmentation:
1) It allows you to use less data: When you have a smaller dataset, you can use image data augmentation to artificially increase its size. This is especially helpful when you do not have enough real-world data available for training your model. Image data augmentation can also be used as a way of regularizing your model so that it does not overfit to the training data.
2) It makes your model more robust: By increasing the variety of training data available to your model, you are also making it more likely that your model will encounter similar images when applied in the real world. This will make your model more robust and less likely to fail when applied in real-world scenarios.
3) It is easier and cheaper than collecting new data: It can be very expensive and time-consuming to collect new training data.Image data augmentation provides a cheaper and easier alternative that can still lead to significant improvements in performance.
What are the Drawbacks of Image Data Augmentation?
Image data augmentation is a technique that is used to artificially increase the size of a training dataset by creating modified versions of images in the dataset. The hope is that by having more data, a deep learning model will be able to learn better representations of the underlying data and therefore achieve better performance on unseen data. However, image data augmentation also has some drawbacks that are worth considering:
1) It can be time-consuming: Depending on the augmentations you choose to use, generating new images can take a significant amount of time. This is especially true if you are working with large images such as high-resolution photographs.
2) It can be expensive: If you are working with high-resolution images, you will need to have a lot of storage space available to save the generated images. Additionally, you will need to have a powerful computer available to generate the images in a reasonable amount of time.
3) It can be hard to control: With some types of augmentations (such as rotation or translation), it can be difficult to control how much an image is modified. This can make it hard to ensure that all the generated images are actually useful for training your model.
4) It can introduce noise: Some types of augmentations (such as adding Gaussian noise) can introduce unwanted artifacts into the generated images which could negatively impact the performance of your deep learning model.
How to Implement Image Data Augmentation?
Image Data Augmentation is a technique that is used to artificially create training data by modifying existing images. The modifications can be simple, like flipping an image horizontally, or more complex like adding noise or changing the colors. Data augmentation is often used in deep learning, where a large amount of training data is necessary to train a model.
There are many ways to implement image data augmentation. One popular method is to use the Python Imaging Library (PIL). PIL allows you to modify images using a variety of methods, including rotation, translation, and resizing. Another popular method is to use the open source computer vision library OpenCV. OpenCV provides functions for a variety of image transformations, including scaling, rotation, and translation.
Data augmentation is an important technique for deep learning because it allows you to train your model on more data than you have available. This can improve the performance of your model and help avoid overfitting.
In this paper, we survey image data augmentation for deep learning. We first briefly overview some previous studies on augmentation policies learned from data. Then, we present several popular augmentation policies used in current deep learning based image classification andGenerative Adversarial Networks training pipelines. In section IV,we survey two representative works which learn data augmentation policies from scratch. Concluding remarks and future directions are given in section V.
D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations (ICLR), 2015.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in International Conference on Learning Representations (ICLR), 2015.
Keyword: A Survey on Image Data Augmentation for Deep Learning