Active Learning Approaches: Strategies, Deep Learning Integration, and Essential Tools

Navigating Active Learning Frameworks for Efficient Labeling

7 min readOct 1, 2023

Introduction

Active learning (AL) attempts to maximize a model’s performance gain while annotating the fewest samples possible. There are situations in which unlabeled data is abundant but manual labeling is expensive. In such a scenario, learning algorithms can actively query the oracle for labels. This type of iterative supervised learning is called active learning. Since the learner chooses the examples, the number of examples to learn a concept can often be much lower than the number required in normal supervised learning. With this approach, there is a risk that the algorithm is overwhelmed by uninformative examples. Recent developments are dedicated to multi-label active learning, hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of machine learning (e.g. conflict and ignorance) with adaptive, incremental learning policies in the field of online machine learning.

Deep Learning (DL) and Active Learning (AL) both fall within the domain of machine learning. DL, also referred to as representation learning, has its roots in the study of artificial neural networks and specializes in the automated extraction of data features. DL boasts robust learning capabilities, thanks to its intricate architecture. However, this complexity also entails a substantial requirement for labeled samples to carry out the corresponding training. This need becomes particularly evident with the proliferation of large-scale datasets containing annotations.

AL, on the other hand, focuses on the examination of datasets and is alternatively known as query learning. AL operates under the premise that distinct samples within the same dataset contribute varying degrees of value to updating the current model. Its objective is to identify and select samples with the highest utility for constructing the training set.

Active Learning

Active Learning (AL) is precisely such a method that is dedicated to exploring ways to maximize performance improvements while minimizing the labeling of samples. To be more specific, its primary aim is to identify the most valuable samples within an unlabeled dataset and present them to an oracle, typically a human annotator, for labeling. This strategy is employed to minimize the cost of labeling as much as possible while still maintaining performance levels. AL encompasses various approaches, including membership query synthesis, stream-based selective sampling, and pool-based AL, tailored to specific application scenarios.

Membership query synthesis allows the learner to request labeling for any unlabeled sample within the input space, even samples generated by the learner itself. Furthermore, the crucial distinction between stream-based selective sampling and pool-based sampling lies in their decision-making processes. Stream-based selective sampling independently assesses whether each sample in the data stream warrants querying for labels of unlabeled samples. In contrast, pool-based sampling selects the optimal query sample based on evaluating and ranking the entire dataset. Notably, research related to stream-based selective sampling primarily targets application scenarios involving small datasets.

2. The Need and Complexity of Combining DL and AL:

Deep Learning (DL) is known for its effectiveness in handling high-dimensional data and automating feature extraction. On the other hand, Active Learning (AL) offers a promising way to reduce the cost of labeling data significantly. The natural step, then, is to merge DL and AL to expand their range of applications, a combination known as DeepAL. This approach capitalizes on the strengths of both methods, and researchers have high hopes for its potential.

However, integrating AL with DL presents challenges due to the following factors:

Model Uncertainty in Deep Learning:

AL often relies on query strategies based on uncertainty, but applying these directly to DL can be problematic. In classification tasks, DL can produce label probability distributions using the softmax layer, but these distributions tend to be overly confident.
The Softmax Response (SR) from the final output can’t always be relied upon as a measure of confidence, making uncertainty-based strategies less effective than random sampling.

2. Scarcity of Labeled Data:

AL typically operates with a limited number of labeled samples for model learning and updates. In contrast, DL typically requires a large volume of labeled data for optimal performance.
The relatively small number of labeled training samples provided by AL methods may not be sufficient to support the training needs of standard DL models.
Moreover, the one-by-one sample querying approach commonly used in AL doesn’t align well with DL requirements.

3. Inconsistent Processing Pipelines:

AL and DL have different processing pipelines. AL algorithms often focus on training classifiers and rely on fixed feature representations for query strategies.
In contrast, DL optimizes both feature learning and classifier training simultaneously. Attempting to adapt DL models within the AL framework or treating them as separate problems can lead to compatibility issues.

The integration of DL and AL offers substantial potential, but overcoming these challenges is crucial to fully leverage the benefits of both approaches in tandem.

3. Active Learning Methods

Active Learning encompasses various strategies and methods aimed at selecting the most informative samples for labeling. Among the most widely used approaches are:

1. Uncertainty Sampling:

Uncertainty sampling is a popular active learning method that seeks to query instances for which the current model is most uncertain about the correct label. This approach includes several techniques:
Least Confidence: Queries samples for which the model exhibits the least confidence in its predictions.
Min-Margin: Selects samples with the smallest margin between the two most likely class probabilities.
Max-Entropy: Targets samples with the highest entropy in class probabilities, indicating high uncertainty.
Deep Bayesian Active Learning (DBAL): Incorporates Bayesian methods to estimate model uncertainty, enhancing uncertainty sampling in deep learning.
Bayesian Active Learning by Disagreement (BALD): Utilizes Bayesian models to measure disagreement among multiple models, selecting samples where the models disagree the most.

2. Diversity Sampling:

Diversity sampling methods aim to create a diverse and representative training set by selecting samples that cover a broad spectrum of the data distribution. Key methods include:
Coreset (greedy): Employs a greedy approach to select a small subset of data points that effectively represents the entire dataset.
Variational Adversarial Active Learning (VAAL): Combines variational autoencoders and adversarial training to select diverse and informative samples.

3. Query-by-Committee Sampling:

Query-by-committee methods involve creating an ensemble of multiple models and selecting samples that lead to the greatest disagreement among these models. One notable method is:
Ensemble Variation Ratio (Ens-varR): Measures the variation in predictions among ensemble members to identify samples where the committee of models has the highest uncertainty.

These active learning methods are instrumental in guiding the selection of samples that are most beneficial for model training while minimizing labeling costs. Depending on the dataset and the problem at hand, one or more of these strategies can be employed to effectively improve model performance and reduce the need for extensive labeled data.

3. Active learning frameworks and tools

CRFsuite: is a conditional random field (CRF) implementation for labeling sequential data. Out of the different implementations of CRFs, the main thrust of this tool is to train and use CRF models as fast as possible, with a simple data format for training and labeling similar to those used in other machine learning tools; where each line consists of a label and features of an item.
modAL: it is an active learning framework for python 3. It is built by scikit-learn, making it flexible and easy to use. modAL precedence comes from the fact that it supports many active learning strategies such as Pool-based sampling, Selective sampling, and query synthesis.
UBIAI: is a robust labeling tool in the field of Natural Language Processing (NLP) that is widely used due to its simple platform and fluidity as it doesn’t require coding knowledge, so it makes it easy to use.
Libact: is Pool-based active learning in Python. Being a python package, it is destined to make active learning easier for general users. The package not only implements several popular active learning strategies but also features the active-learning-by-learning meta-algorithm that assists the users in automatically selecting the best strategy on the fly.
AlpacaTag: is a web-based data annotation framework for sequence tagging. It is a practical tool having an active intelligent recommendation that dynamically suggests annotations and an automatic crowd consolidation thus enhancing real-time inter-annotator agreement by merging inconsistent labels from multiple annotators.
ALiPy: is a module-based-implementation framework that allows users to constantly analyze and evaluate the performance of active learning methods. It is very distinguished for its friendly user interface and its support for more active learning algorithms than any other framework.

Conclusion

Active learning stands as a powerful paradigm within the field of machine learning, offering the potential to significantly enhance model performance while minimizing the need for extensive labeled data. In this comprehensive exploration of active learning, we’ve delved into its core principles, methods, and tools, shedding light on its vital role in addressing the challenges of data labeling and model training. Active learning methods, such as Uncertainty Sampling, Diversity Sampling, and Query-by-Committee Sampling, provide a structured approach for selecting the most informative data points, allowing models to learn efficiently with minimal human annotation effort. These methods enable practitioners to make the most of their labeled data budget, making active learning particularly appealing in scenarios where data labeling is costly or time-consuming..

In conclusion, active learning continues to be at the forefront of machine learning research and application, enabling more efficient use of labeled data and improving the performance of machine learning models. As data-driven tasks become increasingly prevalent in various fields, the adoption of active learning methodologies is poised to play a pivotal role in accelerating progress, reducing costs, and advancing the capabilities of machine learning systems.