Supervised vs Unsupervised Learning for Computer Vision (2022 Guide)

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In the field of computer vision, supervised learning and unsupervised learning are two of the most important concepts. Supervised learning involves a training dataset created by a data scientist to teach a machine how to recognize specific patterns. On the other hand, unsupervised learning does not involve any training data. Instead, it relies on algorithms that detect patterns in data automatically.

In this guide, we will explore the differences and when to use supervised or unsupervised learning for computer vision tasks. We will also discuss which approach is best for specific applications.

 

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What is supervised learning and unsupervised learning in computer vision?

What is supervised learning?

Supervised learning is a type of machine learning algorithm that learns from a set of training data that has been labeled training data. This means that data scientists have marked each data point in the training set with the correct label (e.g., “cat” or “dog”) so that the algorithm can learn how to predict outcomes for unforeseen data and accurately identify objects in new image data.

Typical computer vision tasks of supervised learning algorithms include object detection, visual recognition, and classification.

  • In object detection, supervised learning algorithms are used to learn how to identify and localize objects in images.
  • In image recognition, supervised learning algorithms are used to learn how to identify a particular object category (e.g., “person”, “car”, etc.) from a set of images.
  • And in image classification, supervised learning algorithms are used to learn how to assign a class label (e.g., “cat”, “dog”, etc.) to an image.

Object detection with tracking in a real-time application built on the Viso Suite Platform.

 

 

What is an example of supervised learning?

Common algorithms and techniques in supervised learning include Neural Networks, Support Vector Machine (SVM), Logistic Regression, Random Forest, or Decision Tree algorithms.

How supervised machine learning works

Supervised machine learning is the process of training a model to learn from labelled training data. The model is then able to predict outcomes with new, unlabeled test data. This type of machine learning is called supervised because the data used to train the model is “labeled” with the correct answers.

The supervised learning process can be summarized as follows:

  1. Collect data: This data must be labeled, meaning that it includes the correct answer for each data point (also called ground truth). In computer vision, this is called image annotation, or video annotation to label individual frames. For example, if you want to build a supervised machine learning model to detect specific machine parts, you need to create an image dataset where all the images containing those parts are accurately labeled.
  2. Train a model: The AI model learns to associate certain features with certain labels. For example, the supervised learning model for image classification might learn that images containing a lot of green pixels are likely to be pictures of trees (and therefore likely to be labeled “tree”).
  3. Evaluate the model: After the model has been trained, you can evaluate it to see how well it performs on new, unlabeled data.
  4. Deploy the model: Finally, if the model performs well enough, you can deploy it in a real-world application.

 

Image annotation for supervised learning
Image annotation for supervised learning – A data scientist positions bounding boxes with labels for the class “person”.

 

What is unsupervised learning?

Unsupervised learning is a type of machine learning algorithm that doesn’t require any training data with labels. Instead, unsupervised learning algorithms are fed a set of data and they learn to automatically group similar items together or find patterns in the data.

This means that unsupervised learning algorithms can be used for tasks such as image segmentation, dimensionality reduction, and clustering.

  • In image segmentation, unsupervised learning algorithms are used to automatically group similar pixels together into coherent objects in an image.
  • In dimensionality reduction, unsupervised learning algorithms are used to reduce the number of dimensions in a dataset while preserving most of the information in the data (e.g., for image data compression).
  • And in image clustering, unsupervised learning algorithms are used to automatically group similar items together into clusters.

 

What is an example of unsupervised learning?

Examples of unsupervised methods include algorithms such as K-means clustering, Principal Component Analysis, Hierarchical clustering, or Semantic clustering.

 

How unsupervised machine learning works

The steps in the process of unsupervised training are as follows:

  1. Collect data: Gather relevant data that does not need to be labeled. For example, if you want to build an unsupervised machine learning model to group images of animals, you would need a dataset of images that has been manually sorted into folders containing pictures of dogs, cats, birds, etc. However, it’s important to note that the quality of the results produced by unsupervised learning algorithms can be greatly improved if the data is properly labeled.
  2. Train a model: The model learns to group similar items together or find patterns in the data.
  3. Evaluate the model: After the model has been trained, you can evaluate it to see how well it performs on new, unlabeled data.
  4. Deploy the model: Finally, if the model performs well enough, you can deploy it in a real-world application.

 

Supervised learning versus unsupervised learning: Key differences

In the following, we will discuss the differences between supervision vs. unsupervised learning. There are fundamental characteristic differences between supervised machine learning techniques and unsupervised learning models that determine their usefulness in specific use cases.

 

Difference between supervised and unsupervised learning: Input data

The biggest difference between these machine learning methods is that supervised learning requires a pre-determined and labeled training dataset, while unsupervised learning does not. Hence, supervised learning requires significant upfront human intervention for labelling data appropriately.

For example, in computer vision, annotators label tens of thousands to millions of data points in images. And the data selection and annotation accuracy greatly impact the machine learning model performance. While this helps to train highly effective machine learning models, there are bias risks, and algorithms only run well under precisely define conditions and with data that is very similar to the training data.

 

The machine learning techniques are suitable for different tasks

Supervised learning is used for classification and regression tasks, while unsupervised learning is used for clustering and dimensionality reduction tasks.

A supervised learning algorithm builds a model by generalizing from a training dataset. The goal is to correctly label new data points that the algorithm has not seen before. For example, you can use supervised learning for image classification, where algorithms learn to classify data into different categories (e.g., dog vs cat). A supervised learning algorithm will learn from labeled images of dogs and cats, and then be able to correctly label new images it has not seen before.

An unsupervised learning algorithm, on the other hand, tries to find hidden patterns or intrinsic structures in data. It does not require labeled data; instead, it groups data points together based on similarity. For example, you can use unsupervised learning for image segmentation, where algorithms try to find boundaries between different objects in an image.

 

MS COCO Dataset for supervised training
Datasets such as MS COCO provide a large number of pre-annotated images for supervised training

 

Complexity of supervised vs unsupervised learning

Supervised learning is easier to implement as it has a specific goal- learning how to map input data to target outputs. Unsupervised learning, while also having a defined goal, does not have a specific output that it is trying to achieve and is more focused on understanding the underlying structure of the data.

 

Supervised learning is often more expensive

Another difference between supervised and unsupervised learning is that supervised learning is more expensive than unsupervised learning. This is because training supervised learning models requires labeled data, which must be collected and annotated by humans. Read our article about what computer vision costs.

In general, the data collection and annotation of a large amount of input data is needed to achieve desired results, especially in real-world settings with highly variable objects (e.g. people). On the other hand, training an unsupervised learning model does not require a team of data scientists to label raw data manually.

 

Supervised learning is more accurate in Computer Vision

Finally, supervised learning is usually more accurate than unsupervised learning. This is because supervised machine learning algorithms have a training dataset to learn from, while unsupervised learning algorithms do not.

Training a deep neural network requires multiple iterations and continuous optimization of the model (MLOps) to improve the model’s ability to predict the inferencing results from unexplored data (validation dataset). In most cases, supervised methods can achieve more accurate results.

 

Real-world applications of supervised learning and unsupervised learning?

Unsupervised learning algorithms are used in a wide variety of applications, ranging from medical diagnosis to stock market prediction. In general, supervised learning is more widely used than unsupervised learning because it requires less data and is easier to implement because the output data is predefined.

However, unsupervised learning has its own advantages, such as being more resistant to overfitting (the big challenge of Convolutional Neural Networks) and better able to learn from complex big data, such as customer data or behavioral data without an inherent structure.

 

Examples of supervised learning applications

  • Object recognition: Supervised learning algorithms can be used to locate and categorize objects in images or video (video recognition). They can also be used to identify people, vehicles, and other objects in computer vision systems.

    Abandoned Object Detection
    Abandoned Object Detection in Public Places
  • Text recognition: Supervised learning algorithms can be used to read text in images or videos. Optical character recognition (OCR) systems convert written text into machine-readable form, for example, to read number plates or scan documents with AI.

    OCR application with a numberplate
    OCR application with a number plate recognition.
  • Facial recognition: Face recognition uses deep neural networks trained on databases to identify faces in images or videos. This technology is used for security applications, for example, to unlock phones or gain access to buildings.
  • Visual inspection: Machine learning models are used to inspect products on a production line for defects in industrial manufacturing. This is done by training a supervised machine learning model to distinguish between defective and non-defective items with labelled training data.

    Visual defect detection application
    Visual defect detection application in Manufacturing
  • Analyze churn risk: Machine learning models can be used to assess the likelihood that a customer will discontinue their service with a company. Such models are trained to analyze data from customer surveys, social media, and other sources.
  • Drug discovery: A supervised learning model can be used to find new drug candidates by analyzing data from previous clinical trials. This is done by training a supervised machine learning model to identify patterns in the data that are associated with successful outcomes.
  • Spam detection: ML models can analyze different data points to detect spam emails. Such algorithms are trained on a dataset of emails that have been labeled as spam or not spam. The algorithm learns to distinguish between the two classes of emails by looking at features of the email, such as the subject line, the sender, and the content.

 

Practical applications of unsupervised learning

  • Anomaly detection: Anomaly detection is the process of identifying outliers in a dataset. This can be used for fraud detection, identification of errors in data, and finding unusual patterns. Such big data analysis is important in the Insurance and Finance industries to identify suspicious transactions and detect insider trading.
  • Speech recognition: In the fields of Natural Language Processing (NLP) and Natural Language Understanding (NLU), unsupervised learning is of great importance to improve the understanding of the context of words and phrases.
  • Text sentiment analysis: Algorithms can be used for sentiment analysis to figure out how people feel about something based on their use of words, emoticons and emojis. Such methods are used to analyze sentiment levels for depression detection in social media data.
  • Customer persona: Clustering is used to group similar data points together. This can be used in marketing, for customer segmentation and to identify customer and buyer profiles based on their behavior.
  • Medical imaging: Unsupervised methods allow machines to learn on their own to recognize patterns and abnormalities that supervised learning might not pick up on. It can also be used to segment images so that individual objects can be identified. This is especially useful in medical images where small details can make a big difference.
  • Time series analysis: Time series data is a sequence of data points that are spaced at regular intervals. Unsupervised learning can be used to find patterns in time series data and make predictions about future events based on unlabelled data. This is important for things like weather forecasting, sales prediction, stock market predictions, and predicting traffic patterns.

 

The bottom line: Supervised vs unsupervised learning

The biggest differentiation between supervised and unsupervised methods is that supervised models require labelled input. With supervised learning, we are feeding the machine known information so that it can learn to find such patterns and make predictions. Unsupervised learning takes an unlabeled dataset and tries to automatically identify structure and patterns within it.

The benefit of supervised learning is that you can train your model to be much more accurate in solving a clearly specified problem with limited output options (classification problems). Unsupervised learning is more exploratory and doesn’t require pre-labelled data, making it more flexible. It can be used to segment customers, find relationships, and detect anomalies.

Both supervised and unsupervised learning are important tools in the machine learning toolbox for data analysis. Deciding which one to use depends on the nature of the problem you are trying to solve and the available data volume and type.

 

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