Data Augmentation
The best way to make a machine learning model generalize better is to train it on more data. Of course, in practice, the amount of data we have is limited. One way to get around this problem is to create fake data and add it to the training set.For some machine learning tasks, it is reasonably straightforward to create new fake data.
A common trick to reduce overfitting in convolutional neural networks is the idea of data augmentation. In data augmentation, new training examples are generated by using transformations on the original examples. Although it works better in some domains than others. Image processing is one domain to which data augmentation is very well suited. This is because many transformations such as translation, rotation, patch extraction, and reflection do not fundamentally change the properties of the object in an image. However, they do increase the generalization power of the data set when trained with the augmented data set. For example, if a data set is trained with mirror images and reflected versions of all the apples in it, then the model is able to better recognize apples in different orientations.
Many of these forms of data augmentation require very little computation, and therefore the augmented images do not need to be explicitly generated up front. Rather, they can be created at training time, when an image is being processed. For example, while processing an image of an apple, it can be reflected into a modified apple at training time. Similarly, the same apple might be represented in somewhat different color intensities in different images, and therefore it might be helpful to create representations of the same image in different color intensities. In many cases, creating the training data set using image patches can be helpful. Although most data augmentation methods are quite efficient, some forms of transformation that use principal component analysis (PCA) can be more expensive. PCA is usedin order to change the color intensity of an image. If the computational costs are high, it becomes important to extract the images up front and store them. The basic idea here is to use the 3 × 3 covariance matrix of each pixel value and compute the principal components. Then, Gaussian noise is added to each principal component with zero mean and variance of 0.01. This noise is fixed over all the pixels of a particular image. The approach is dependent on the fact that object identity is invariant to color intensity and illumination. .
One must be careful not to apply data augmentation blindly without regard to the data set and application at hand. For example, applying rotations and reflections on the MNIST data set of handwritten digits is a bad idea because the digits in the data set are all presented in a similar orientation. Furthermore, the mirror image of an asymmetric digit is not a valid digit, and a rotation of a ‘6’ is a ‘9.’ .Optical character recognition tasks require recognizing the difference between ‘b’ and ‘d’ and the difference between ‘6’ and ‘9’, so horizontal flips and 180◦ rotations are not appropriate ways of augmenting datasets for these tasks.
Injecting noise in the input to a neural network (Sietsma and Dow, 1991) can also be seen as a form of data augmentation. For many classification and even some regression tasks, the task should still be possible to solve even if small random noise is added to the input. Neural networks prove not to be very robust to noise. One way to improve the robustness of neural networks is simply to train them with random noise applied to their inputs. Input noise injection is part of some unsupervised learning algorithms such as the denoising autoencoder . Noise injection also works when the noise is applied to the hidden units, which can be seen as doing dataset augmentation at multiple levels of abstraction.
The key point in deciding what types of data augmentation are reasonable is to account for the natural distribution of images in the full data set, as well as the effect of a specific type of data set augmentation on the class labels.
When comparing machine learning benchmark results, it is important to take the effect of dataset augmentation into account. Often, hand-designed dataset augmentation schemes can dramatically reduce the generalization error of a machine learning technique. To compare the performance of one machine learning algorithm to another, it is necessary to perform controlled experiments. When comparing machine learning algorithm A and machine learning algorithm B, it is necessary to make sure that both algorithms were evaluated using the same hand-designed dataset augmentation schemes. Suppose that algorithm A performs poorly with no dataset augmentation and algorithm B performs well when combined with numerous synthetic transformations of the input. In such a case it is likely the synthetic transformations caused the improved performance, rather than the use of machine learning algorithm B. Sometimes deciding whether an experiment has been properly controlled requires subjective judgment. For example, machine learning algorithms that inject noise into the input are performing a form of dataset augmentation. Usually, operations that are generally applicable (such as adding Gaussian noise to the input) are considered part of the machine learning algorithm, while operations that are specific to one application domain (such as randomly cropping an image) are considered to be separate pre-processing steps.
As described in section , it is easy to improve the generalization of a classifier by increasing the size of the training set by adding extra copies of the training examples that have been modified with transformations that do not change the class. Object recognition is a classification task that is especially amenable to this form of dataset augmentation because the class is invariant to so many transformations and the input can be easily transformed with many geometric operations. As described before, classifiers can benefit from random translations, rotations, and in some cases, flips of the input to augment the dataset. In specialized computer vision applications, more advanced transformations are commonly used for dataset augmentation. These schemes include random perturbation of the colors in an image and nonlinear geometric distortions of the input.
Augmentation techniques for different data types –
Image: The augmentation techniques for images allow scaling, flipping, rotating, shearing, cropping, change in brightness/contrast/sharpness/blur, color filtering, and many more.
Text: The augmentation techniques for text support NLP tasks by word/sentence shuffling, word replacement with synonyms, paraphrasing, and so on.
Audio and Video techniques can manipulate the data by introducing noise, shifting,changing speed, etc.
Note: There are multiple python data augmentation libraries for image and text(Augmentor, Imgaug, AutoAugment, lbumentations, Keras ImageDataGenerator, OpenCV, Skimage).The library AugLy is a recently launched Python open-source project by Facebook for data augmentation. AugLy aims to increase the robustness of AI models during training and evaluation. The library includes four modes (audio, video, image, and text) and provides over 100 data augmentation methods.SOLT is a Deep Learning data augmentation library that supports images, segmentation masks, labels, and key points. SOLT is also fast and has OpenCV in its backend.
Data Augmentation using Augmentor ( example)
# Importing necessary library
import Augmentor
# Passing the path of the image directory
p = Augmentor.Pipeline("image_folder")
# Defining augmentation parameters and generating 5 samples
p.flip_left_right(0.5)
p.black_and_white(0.1)
p.rotate(0.3, 10, 10)
p.skew(0.4, 0.5)
p.zoom(probability = 0.2, min_factor = 1.1, max_factor = 1.5)
p.sample(5)
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