Active Learning: a targeted and effective strategy for labeling data

💡 Active Learning : more than just a machine learning approach. Discover in our article how this technique is used to improve data annotation processes in artificial intelligence.

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Introduction

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Artificial Intelligence (AI) has seen remarkable advances in recent years, especially in the fields of Computer Vision and Natural Language Processing (NLP). However, the performance of these systems depends heavily on the quality and quantity of the labelled data used for their training. Annotating data, whether images or text, is a critical step in providing that labelled data.

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Active Learning, a targeted machine learning approach, can speed up the data annotation process, make the work of annotators simpler, and can be effectively combined with outsourcing Data Labelling work to Madagascar. In addition, many online image annotation tools include Active Learning capabilities.

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💡 Accelerate your AI projects by combining outsourcing in Madagascar to a targeted strategy to facilitate the work of annotators

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Active Learning: Optimizing Data Annotation

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Active Learning is a machine learning strategy that aims to intelligently select the most informative and relevant samples for annotation in order to prepare the data needed to train artificial intelligence models. Instead of following a passive learning approach where data is labelled at random, Active Learning uses AI models in the process of learning to identify which samples are the most difficult to classify or are uncertain.

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These samples are then submitted to human annotators for the addition of labels, thus gradually improving the performance of the model, while providing annotators with indications or tips to more effectively label the data sets given to them.

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Data Labeling Outsourcing in Madagascar

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Madagascar quickly established itself as a preferred destination for outsourcing data labelling for several reasons. First of all, the country offers competitive labor costs, making outsourcing more economical. In addition, Madagascar has a multilingual population, making it a smart choice for text annotation projects in various languages.

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In addition, the availability of technical expertise in the field of computer science and related sciences makes it possible to find qualified annotators. Finally, the country's political stability creates a favourable environment for foreign investment, thus strengthening its attractiveness for outsourcing data labelling.

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Annotating images and texts

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Annotating images and texts is essential for training AI models for various tasks.

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Image annotation

In the field of Computer Vision, image annotation consists in classifying images, to add labels or tags to objects and elements in an image. For example, for object detection, bounding boxes can be drawn around objects of interest.

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Text annotation

In NLP (“Natural Language Processing”), text annotation involves identifying and labeling specific parts of a text. This may include detecting named entities, categorizing feelings, or categorizing topics.

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Annotators: key players for the effective use of Active Learning

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Annotators, also known as “Data Labelers”, play a central role in the Active Learning process. Their expertise in understanding specific instructions and in applying strict labeling standards ensures the quality of the annotated data.

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Close collaboration between Data Scientists, developers, and annotators is essential to adapt the annotation process to the changing needs of the model being learned.

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Online image and text annotation tools with Active Learning capabilities

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Several online image annotation platforms now offer Active Learning capabilities, simplifying the annotation process for Data Labelers.

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For example, UBI-AI is a platform that offers online image annotation tools with Active Learning functionalities for NLP projects. These tools allow annotators to intelligently select the most informative and uncertain samples, improving the efficiency of the annotation process.

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Besides UBI-AI, other examples of platforms that include Active Learning for online image annotation include Encord and Labelbox. Like most annotation platforms now, these tools provide a user-friendly interface for tagging images with various types of labels, such as key points, bounding boxes, and object segments.

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Conclusion

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Active Learning is a powerful approach to accelerate the data annotation process in the field of Artificial Intelligence. By outsourcing data labelling to Madagascar, businesses benefit from competitive costs, available technical skills, and a qualified multilingual population.

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Thanks to annotators and online image annotation tools with Active Learning capabilities, AI projects can reach higher levels of precision and performance, taking the AI development process to new heights.