Active learning in machine learning | Use cases and Frameworks

Active learning in machine learning | Use cases and Frameworks

Sat Aug 26 2023

In today's modern world, deep learning services is used as the real algorithmic backbone for all computer vision tasks, including image classification and segmentation, scene reconstruction, and image synthesis. In general, the basis for the success of most algorithms is the use of large amounts of labeled data of good quality. Data labeling is a very difficult process that takes about 70% of the time allocated to a machine learning project. Even in this situation, some labels may be incorrect, which has a negative impact on model training. Therefore, today's methods focus on reducing the need for labeled training data and leveraging the large amount of unstructured data available in this era.

Active learning in machine learning is a low supervision method that belongs to the class of semi-supervised learning. It is a learning paradigm in which a small amount of labeled data and a large amount of unlabeled data are used together to train a model.

But what exactly is active learning in machine learning and why is it called active? Let's take a closer look.

What is Active learning in machine learning?

In active learning in machine learning, the user is interactively asked to annotate data with desired results. In active learning in machine learning, the algorithm actively selects examples to be labeled from the unlabeled data set. An important principle of active learning is that an ML algorithm can achieve a higher level of accuracy with a smaller number of training labels, if it is allowed to choose the data it wants to learn from.

What is Active learning machine learning?

How does Active learning in machine learning work?

Active learning machine in learning works by giving the algorithm a starting point of labeled data. This forced selection is active sampling, and the goal of the learner is to select as few samples as possible. The more it selects, the greater the risk of mislabeling the samples and degrading performance in future predictions.

Because active learning focuses on the data sets that are most likely to produce better predictions, machine learning models are better at learning from data sets with high uncertainty. The four common types of active learning algorithms are:

Selective Sampling, Iterative Refinement, Uncertainty Sampling, Query by Committee

How does Active learning in machine learning work?
  • Selective sampling: The algorithm randomly selects a small number of samples from the data set, labels them, and then uses these samples to train the model.
  • Iterative refinement: The algorithm first selects a random sample as the active sample and then compares the prediction error in that sample with the prediction error in all other samples in the data set. If it finds a more accurate prediction in another case, it reclassifies that case as the active case and repeats the process.
  • Uncertainty sampling: The program randomly selects a small number of samples from the data set, labels them, and then uses these samples to train the model. This technique is called uncertainty sampling.
  • Query by Committee: The algorithm begins by selecting a random sample as the active sample. Next, it divides the data set into clusters, each containing samples comparable to the active sample. Finally, it selects an active sample from each cluster.

How is active learning different from reinforcement learning?

Reinforcement learning and active learning can both reduce the number of labels needed for models, but in general, these two concepts have differences that we will explain in this section:

  • Reinforcement learning: This goal-oriented approach inspired by behavioral psychology allows you to receive input from the environment. This means that the agent gets better and learns during use. This approach is similar to how people learn from their mistakes. Humans work with a reinforcement learning approach. There are no training steps because the agent instead learns through trial and error and uses a predetermined reward system that provides input about the optimality of a particular action. This learning does not need to be fed because it generates its data over time.
  • Active learning: Active learning is closer to the traditional learning approach with supervision. This approach is a type of semi-supervised learning; that is, models are trained using labeled and unlabeled data. The idea behind this approach is that patching a small sample of data may result in the same or better accuracy than fully labeled training data. The only difficult part is figuring out what that sample is. During the training phase, active learning in machine learning labels the data incrementally and dynamically so that the algorithm may discover which labels are most helpful for learning.

How is active learning different from reinforcement learning?

Active learning in machine learning use cases

Active learning in machine learning has wide practical applications in all fields of artificial intelligence due to its ability to produce optimal performance even with a few labeled samples. Active learning is used instead of traditional supervised learning and saves a lot of resources for ML teams. In the rest of this section, we will discuss some of these things:

Computer Vision

Computer vision includes methods such as image classification and segmentation, object recognition, image recovery, and enhancement. Due to the abundance of unlabeled data that is accessible via the Internet, active learning in machine learning is commonly used in this field. Some of these programs are as follows:

Computer Vision

Image classification is the process of determining the categories to which an image belongs. For example, the CEAL model, or cost-effective learning, applies active learning in the field of image classification, which, unlike conventional approaches that consider only the most informative and famous examples, automatically suggests examples without Labels can be selected and pseudo-annotated.

Object detection: Object detection is a computer vision technique that involves screening out salient objects from an image. This program is different from classification; in fact, in image classification, the whole image is assumed to belong to one class, but in object detection, a single image contains several local areas belonging to different classes. An example of active learning that is used for object recognition is the MI-AOD model or multiple active object recognition model. With the help of invariance learning and multi-sample learning, this model selects the training images from the unlabeled set to have the user's label. A feature pyramid network calculates the uncertainty in unlabeled set prediction.

     
   

Natural Language Processing

Natural language processing is a subset of artificial intelligence that can understand natural human language. This process includes tasks such as completing the text and recognizing the emotions of the text and others. Active learning has been widely used for various NLP tasks due to its popularity in computer vision.

For example, active learning can be used for the problem of neural machine translation (NMT), which uses deep learning models to translate text from one language to another.

Natural Language Processing

Audio processing

The audio processing includes important automated tasks such as speech-language recognition, voice completion, voice synthesis, etc. With the help of active learning, you can solve problems related to speech recognition.

Audio processing

Frameworks Used for Active Learning

Some of the famous and popular Active learning in machine learning frameworks are as follows:

  • ModAL: This is an active learning framework for Python3 designed with modularity, flexibility, and extensibility in mind. This software is based on scikit learning and allows you to create active learning streams very quickly with complete freedom.
  • libact: This is a Python package designed to make active learning easier for real-world users. In addition to implementing several well-known active learning strategies, this package also provides active learning meta-strategy learning that helps the machine learn the best strategy automatically.
  • AlpacaTag: This is a framework for collective annotations based on active learning for sequence tagging, including Named Entity Recognition (NER). The advantages of this framework are described below:
  1. Active and intelligent recommendation: Dynamic annotation suggestion and selection of the most instructive unlabeled samples.
  2. Automatic crowd consolidation: Increasing inter-annotator agreement in real-time by merging conflicting labels from multiple annotators.
  3. Real-time model deployment: While fresh annotations are being created, users can deploy their models to systems farther down the pipeline.

Why do we need active learning?

Manually labeling an entire dataset can be costly and time-consuming for many organizations, so people are turning to semi-supervised and unsupervised ML approaches. An active learning approach can specifically help you in the following situations:

  • Your AI solution requires a long time to market, and manual data labeling can expose your project to various risks.
  • You don't have money to pay data scientists or SMEs to manually tag all your data.
  • Your team doesn't have enough people to manually tag all your data.
  • You have a large collection of unlabeled data.
Why do we need active learning?

Active learning in machine learning can be much more cost-effective and faster than traditional supervised learning, but you still need to consider the computational costs and iterations required to achieve an efficient model. When this method is used well, it can be as good as the quality and accuracy of traditional methods. Having technical skills and expertise in this field can be very important because the sampling method chosen can make or break the effectiveness of the active learning approach.

The advantages of active learning computer vision

Active learning computer vision introduces a range of advantages that address common challenges associated with traditional supervised learning methods. Here are the key benefits of adopting active learning in the realm of computer vision:

  • Optimized Data Labeling Efficiency: Active learning strategically selects the most informative data points for annotation, maximizing the learning efficiency of the model. Rather than labeling a large, indiscriminate dataset, active learning focuses on instances where the model is uncertain or likely to benefit the most from additional information. This targeted approach significantly reduces the amount of labeled data required for model training.
  • Reduced Annotation Costs: By prioritizing the labeling of the most informative samples, active learning helps minimize the overall cost associated with acquiring labeled data. In computer vision, where annotation can be labor-intensive and expensive, this is a substantial advantage. The selection of critical data points for annotation optimizes resources, making the labeling process more cost-effective while maintaining or even improving model performance.
  • Enhanced Model Generalization: Active learning contributes to improved model generalization by focusing on diverse and challenging examples during the training process. By actively seeking instances that challenge the current model's understanding, the learning algorithm becomes more robust and better equipped to handle unseen scenarios. This results in a model that performs well on a broader range of inputs, leading to increased real-world applicability.
  • Adaptability to Model Uncertainty: Computer vision models often encounter ambiguity in real-world scenarios. Active learning excels in handling uncertain situations by prioritizing the labeling of instances where the model exhibits uncertainty. This adaptability is particularly valuable when dealing with complex scenes, diverse datasets, or novel objects, ensuring that the model learns from the most challenging and informative examples.
  • Iterative Model Improvement: Active learning facilitates an iterative process where the model continuously evolves with each round of labeling. As the model gains more information from the newly annotated data, it becomes increasingly adept at making accurate predictions. This iterative improvement is especially advantageous in dynamic environments where the data distribution may change over time.
  • Human-in-the-Loop Collaboration: Active learning encourages a collaborative approach between automated algorithms and human annotators. By leveraging human expertise to label challenging samples, active learning combines the strengths of machine learning models and human intuition. This human-in-the-loop synergy further refines the model's performance, fostering a more effective and collaborative learning process.
The advantages of active learning computer vision

The advantages of active learning computer vision extend beyond efficiency gains to include cost reduction, improved model generalization, adaptability to uncertainty, and a collaborative human-in-the-loop approach. These benefits make active learning a compelling strategy for enhancing the performance of computer vision models, particularly in scenarios where labeled data is a valuable and limited resource.

Conclusion

Active learning is a technique where the machine decides which are the most important data points to be labeled by humans. This technique has several advantages over traditional machine learning methods. However, much research is still being done to determine which tasks and data sets suit active learning approaches. One of the questions is whether active learning is always better than traditional methods. More research is needed to answer this question. It is also unknown how well active learning performs with increasing data size. So, more research is needed to understand this approach's benefits and limitations better. Overall, however, this area is very promising and has great potential to improve the accuracy of machine learning models.

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