Data annotation plays a fundamental role in the preparation of data for artificial intelligence (AI) and machine learning (ML) projects. It involves labeling, categorizing or annotating data to enable machine learning algorithms to understand and generalize from it. Ever wondered what the main differences are between supervised and unsupervised learning? The different techniques for annotating unstructured data (annotating images, audioaudio extracts or videos)? That's exactly what we're going to explore in this article, highlighting the essential differences between these two approaches.

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Supervised learning: introduction

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Supervised learning is a type of machine learning in which the AI algorithm is trained on a set of labeled data. This means that each data example used for training is associated with a label or class. The aim is for the algorithm to learn to correctly associate input data with output labels based on the annotated data examples provided.

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When annotating data for supervised learning, image, video or text annotators (otherwise known as Data Labelers) assign specific labels or categories to the data based on what it represents. For example, in an image classification task, each image is labeled with the class to which it belongs, such as "cat", "dog", "car" and so on. This careful labeling enables the algorithm to learn how to correctly associate data features with the appropriate categories, paving the way for precise, high-performance applications of artificial intelligence.

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Different supervised learning models

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There are a number of different supervised learning models that can be implemented in the form of mathematical and then computer algorithms. These models differ in their approach to training with data, and in the type of label to be predicted, be it a continuous value or a class.

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One of the most popular supervised learning techniques for predicting continuous values is linear regression. For example, imagine you want to predict the yield of an agricultural crop as a function of variables such as rainfall, temperature and soil quality. Linear regression can be used to estimate yield as a function of these different factors. Although this model is effective in capturing linear relationships between the explanatory variables and the variable to be predicted, notably thanks to its variants which incorporate regularization to avoid over-learning, it reaches its limits when the relationships between variables become more complex than simple linearities.

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In the field of classification, which is another supervised task, we come across several models, including those based on decision trees such as RandomForest, variants of regression such as logistic regression, as well as support vector machines (SVMs).

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However, supervised learning is not limited to these algorithms, even though they represent the state of the art in classical machine learning. Deep Learning, based on deep neural networks, is increasingly being used for supervised learning, particularly for complex problems such as the classification of unstructured data (images, sounds, videos), or to achieve better performance in classical Machine Learning problems.

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Other supervised learning models include artificial neural networks, convolutional neural networks and recursive neural networks. While we've just scratched the surface (and popularized) these concepts, which are important to grasp, including in the world of Data, don' t hesitate to consult this article from DataScientest to find out more.

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Unsupervised learning: another paradigm

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Unsupervised learning is distinguished by a different approach, particularly when it comes to data "management". In the context of unsupervised learning, algorithms don't need labeled data examples to learn (at least, not labeled with intelligible labels as may be the case in annotation for supervised models). As part of their training, the models explore the data for intrinsic structures or patterns, without any prior indication of the associated categories or labels. Common unsupervised learning tasks include data segmentation, anomaly detection and clustering. In short, the data annotation strategy is completely different, and the data volumes sometimes smaller.

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You might say... so it's possible to build models with a limited amount of data. Sounds too good, doesn't it? It's important to note that unsupervised learning has its limitations. In the absence of specific labels, it may be more difficult to obtain a clear interpretation of the results. The groupings identified may not correspond to real categories, and the quality of the analysis largely depends on the quality of the raw data. What's more, the absence of supervision can sometimes make it difficult to validate results, which can be problematic in fields where precision is crucial (e.g., medicine).

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Key differences between these two approaches, particularly in terms of data annotation requirements

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Now that we've introduced the concepts, let's look at the main differences between data annotation for supervised and unsupervised learning:

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Type of label

In supervised learning, labels are specific and clearly designate the categories to which the data belong. In unsupervised learning, annotators generally don't assign explicit labels, leaving the algorithm to discover structures or similarities on its own.

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Objectives

Supervised learning aims to teach the algorithm to predict labels for new data, while unsupervised learning aims to discover hidden structures or clusters within the data.

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Application examples

supervised learning is commonly used in classification classificationregression and object object detection. Unsupervised learning is used for segmentation, dimension reduction, anomaly detection and clustering.

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Complexity of annotations

Image or video annotation for supervised learning is generally more demanding, as it requires prior knowledge of categories, and often functional expertise. Data annotation for unsupervised learning may be less demanding in terms of expertise, but for certain techniques, requires more processing time for a smaller volume (e.g. segmentation).

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In conclusion...

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Choosing the right approach to data annotation depends on your project objectives and the types of algorithms you wish to use. By understanding these differences, you'll be better prepared to plan and execute your image, audio/video or text annotation tasks successfully.

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To support you in the complex process of data processing, from data collection to annotation and validation of results, Innovatiana has positioned itself as a service provider.

re high-quality data annotation services, capable of meeting the needs of both paradigms, whether for supervised or unsupervised learning.

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With our expertise in data annotation complemented by functional expertise for the most complex tasks, as well as specific knowledge of the main labeling tools, we're ready to provide you with quality data to feed your AI projects, whatever approach you prefer! Remember: it's by building quality training data sets that you get better AI models!