Data annotation for supervised vs. unsupervised learning: what are the differences?
🔎 Data annotation is an important part of data preparation for artificial intelligence (AI) and machine learning (ML) projects. It consists of labeling, categorizing, or annotating data to allow machine learning algorithms to understand and generalize from this data. Very often, it is associated with a task that is not very complex, repetitive, sometimes thankless,... but to scale up and build usable data sets for supervised learning, we need to rethink this discipline.
Supervised learning: what is it? Have you ever wondered what the main differences are between supervised and unsupervised learning? The various techniques for annotating unstructured data (annotations of images, audio snippets pr videos)? That's exactly what we're going to explore in this article, shedding light on the key differences between these two approaches.
Supervised learning: introduction
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 sample data used for training is associated with a label or class. The aim is for the algorithm to learn how to correctly associate input data with output labels based on the annotated sample data provided.
When annotating data for supervised learning, image, video, or text annotators (otherwise known as Data Labelers) assign specific labels or categories to data based on what they represent. For example, in an image classification task, each image is labeled with the class to which it belongs, such as “cat”, “dog”, “car”, etc. This careful labeling allows the algorithm to learn how to correctly associate data characteristics with the appropriate categories, thus paving the way for accurate and efficient applications of artificial intelligence.
Different supervised learning models
There are various supervised learning models that can be implemented in the form of mathematical and then computer algorithms. These models are distinguished by their approach to training using data and the type of label to be predicted, whether it is a continuous value or a class.
One of the most popular supervised learning techniques for predicting continuous values is linear regression. For example, let's say you want to predict the yield of an agricultural crop based on variables such as the amount of rain, temperature, and soil quality. Linear regression can be used to estimate performance based on these various factors.
Although this model is effective in capturing linear relationships between the explanatory variables and the variable to be predicted, in particular thanks to its variants that incorporate regularization to avoid over-learning, it reaches its limits when the relationships between the variables become more complex than simple linearities.
In the field of classification, which is another supervised task, we can encounter several models, including those based on decision trees such as RandomForest, regression variants such as logistic regression, as well as support vector machines (SVM).
However, supervised learning is not limited to these algorithms, although they represent the state of the art in classical machine learning. Deep Learning, which is based on deep neural networks, is increasingly used for supervised learning, especially in the case of complex problems such as the classification of unstructured data (images, sounds, videos) or to obtain better performances in classic Machine Learning problems.
Other supervised learning models exist, including artificial neural networks, convolutional neural networks, or recursive neural networks. If we are just touching on (and popularizing) these concepts that are important to understand, including in the world of Data, feel free to check out this DataScientest article to learn more.
Unsupervised learning: another paradigm
Unsupervised learning is characterized by a different approach, especially when it comes to “managing” data. In the context of unsupervised learning, algorithms do not need examples of labeled data to learn (in any case, not labeled with intelligible labels as can be the case in annotation for supervised models).
As part of their training, models explore data in search of intrinsic structures or models, without prior indications 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 are sometimes smaller.
You will say... it is therefore possible to build models with a limited amount of data. Sounds too good, right? It is important to note that unsupervised learning has limitations. Without specific labels, it can be more difficult to get a clear interpretation of the results. The groupings identified may not correspond to real categories, and the quality of the analysis depends largely on the quality of the raw data. In addition, the lack of supervision can sometimes make it difficult to validate results, which can be problematic in areas where precision is crucial (for example, in the field of medicine).
Key differences between these two approaches, especially with respect to data annotation needs
Now that we've introduced the concepts, let's look at the key differences between data annotation for supervised and unsupervised learning:
Nature of labels
In supervised learning, labels are specific and clearly identify the categories to which the data belongs. In unsupervised learning, annotators generally do not assign explicit labels, leaving the algorithm to discover structures or similarities by itself.
Objectives
Supervised learning aims to teach the algorithm to predict labels for new data, while unsupervised learning aims to discover hidden structures or groupings within the data.
Examples of applications
supervised learning is commonly used in classification tasks, regression and object detection. Unsupervised learning is used for segmentation, dimension reduction, anomaly detection, and clustering.
Complexity of annotations
Annotating images or videos for supervised learning is generally more demanding because it requires prior knowledge of categories, and often functional expertise. Annotating data for unsupervised learning may be less demanding in terms of expertise, but for some techniques, requires more processing time for a smaller volume (example: segmentation).
In conclusion...
Choosing the right data annotation approach depends on the goals of your project and the types of algorithms you want to use. By understanding these differences, you will be better prepared to plan and execute your image, audio/video, or text annotation tasks successfully.
To support you in the complex process of data processing, from collection, to the annotation and validation of results, at Innovatiana, we position ourselves, at Innovatiana, as a provider of high-quality data annotation services, capable of meeting the needs of both paradigms, whether for supervised or unsupervised learning.
With our expertise in creating high-quality datasets (i.e. in data annotation, but not only) complemented by functional expertise for the most complex tasks, as well as specific knowledge of the main labelling tools, we are ready to assemble quality data to feed your artificial intelligence projects, regardless of the approach you prefer ! Remember: building quality training data sets is the way to get better AI models.
