Data Augmentation
What is Data Augmentation?
Data augmentation is a powerful technique in AI that helps improve model performance by expanding the available training data without collecting new real-world samples. In AI automation, this process involves creating variations of existing data through methods like rotating images, adding noise, or paraphrasing text.
For Lleverage and similar platforms, data augmentation is particularly valuable when working with limited datasets, as it helps create more robust and generalizable AI models. For instance, in document processing, a single invoice template could be augmented by varying fonts, layouts, or adding different types of noise to simulate real-world variations.
Why is Data Augmentation important?
Understanding data augmentation is crucial for businesses looking to implement AI solutions with limited data resources. It enables organizations to build more reliable AI models without the costly and time-consuming process of collecting additional real-world data. In the context of workflow automation, data augmentation helps create more resilient models that can handle variations in input data, leading to more consistent and accurate results in production environments. This is particularly relevant for Lleverage users who need to automate complex processes with limited initial data.
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How you can use Data Augmentation with Lleverage
A company uses Lleverage to automate invoice processing but only has a small sample of past invoices. Through data augmentation, they can create variations of these invoices by changing layouts, fonts, and adding realistic noise or artifacts. This expanded dataset helps train a more robust model that can accurately process invoices from new vendors or in different formats, significantly improving the automation workflow's reliability.
Data Augmentation FAQs
Everything you want to know about
Data Augmentation
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Data augmentation creates variations of existing real data, while synthetic data generation creates entirely new artificial data from scratch. Augmentation maintains the core characteristics of the original data while introducing controlled variations.
Consider using data augmentation when you have a limited dataset but need your model to handle various real-world scenarios, or when you notice your model performing poorly on slightly different versions of your training data.
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