When given enough training data, machine learning algorithms can do amazing feats. Unfortunately, many applications still struggle to access high-quality data.

Making copies of current data and making minute modifications is one method for increasing the diversity of the training dataset. It is called “data augmentation.” Data augmentation is a low-cost and adequate approach to improving the performance and accuracy of machine learning models in data-constrained scenarios.

For example,

Let us suppose your image classification dataset has ten images of cats. You have increased the number of cats for the “cat” class by making duplicates of your cat images and turning them horizontally. Rotation, cropping, and translation are some of the additional changes available. You can also combine the changes to increase the number of unique training instances in your collection.

The process of changing, or "augmenting,” a dataset with extra information is known as data augmentation. This additional input might range from images to text, and its integration into machine learning algorithms increases their productivity. Data augmentation techniques artificially create many versions of the dataset to increase the amount of a real dataset. Computer vision and natural language processing (NLP) models use data augmentation tactics to address data scarcity and a lack of data diversity. Data augmentation is not restricted to images and can also be used on other forms of data. In in-text datasets, we can change nouns and verbs using synonyms . It is possible to adjust training examples in audio data by adding noise or adjusting the playback speed.

Data Augmentation techniques in Computer Vision:

Some of the methods for data augmentation that are frequently used are:

• Noise Addition

To the existing images, add gaussian noise.

• Cropping

A portion of the image is selected, cropped, and resized to its original size.

• Flipping

We can flip the image horizontally and vertically. Flipping rearranges the pixels while protecting the features of the image.

• Rotation

We can rotate the image by a degree ranging from 0° to 360°. In the model, each rotated image will be unique.

• Scaling

The image is scaled outward and inward. When scaled outward, the image size increases, whereas when scaled inward, the image size decreases.

• Translation

We can shift the image into various locayions along the x-axis and y-axis. 

• Brightness

The image’s brightness is changed, and the new image will be darker or lighter. This technique enables the model to identify images in various lighting conditions.

• Contrast

Here contrast of the image is changed, and the new image will be different from the luminance and color aspects. The following image’s contrast is changed randomly.

• Color Augmentation

Here the color of the image is changed by new pixel values. There is a grayscale example image.

• Saturation

The depth or intensity of color in an image is called saturation. The data augmentation process has saturated the image below.

Conclusion

TagX tried to provide an overview of several data augmentation approaches and demonstrated how data augmentation techniques are frequently used in combination, for example, cropping after resizing. So it is important to note that data augmentation helps to boost training data size and machine learning model performance.

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