Machine Learning with Small Data: When Big Data is not available

For over a decade, the explosion in data generation is driving the emergence and the rise of machine learning (ML) and artificial intelligence (AI). Modern industrial organizations are increasingly developing and deploying infrastructures that enable them to manage Big Data. Accordingly, they leverage Big Data to build powerful machine learning systems. In this context, machine learning models are mostly designed to consume vast amounts of information to improve their performance. However, obtaining large datasets is at times challenging, expensive, and time-consuming. In many cases organizations do not have access to large volumes of data about the business problem that they want to solve through machine learning. There are also cases where data are available, yet not properly collected, organized, and managed. Therefore, there is a very good business motivation behind the development and deployment of machine learning algorithms that can produce good outcomes even when trained with minimal data. Such algorithms are conveniently characterized as “data efficient” machine learning methods and strive to achieve high performance without large volumes of training data. Data efficient ML algorithms make it possible to develop models in situations where data is scarce or costly. There are several techniques that enable data-efficient machine learning, including transfer learning, active learning, few-shot learning, data augmentation, and machine learning pruning techniques.

Transfer Learning: Leveraging Pre-trained Models

Transfer learning is a technique that allows a model trained on one task to be fine-tuned for a different, but related, task. This method is particularly useful when you have limited data for the target task, as it leverages the knowledge acquired from a larger dataset. For instance, in image recognition tasks, pre-trained convolutional neural networks (CNNs) on vast datasets like ImageNet can be used as feature extractors. These networks have already learned to identify useful features from images, such as edges and textures. By using these pre-trained models and fine-tuning them on your smaller dataset, it is possible to achieve high performance with less data.

Other prominent examples of transfer learning models are found in natural language processing (NLP) tasks. Specifically, there are pre-trained language models that can be fine-tuned on a specific task with limited data. Prominent examples of such models are  BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer).These models have been trained on massive text corpora and have learned the structure of the language. Hence, they can be fine-tuned for tasks like sentiment analysis, machine translation, and text classification.

Active Learning: Selecting Informative Samples

Active learning is a machine learning method that involves iteratively selecting the most informative samples from the available data pool for labeling and model training. In this approach, the ML model actively participates in the data selection process towards identifying the samples that would provide the most significant improvement in its performance.

One of the most common active learning strategies is uncertainty sampling. In this strategy the model selects instances for which it has the least confidence in its predictions. These instances are then labeled by a human expert and added to the training set. This process is repeated until a desired performance level is reached. In essence, the machine learning model consults an authoritative source (i.e., a human expert) towards accelerating the data labelling process and operating based on minimal data.

By focusing on the most informative samples, active learning can dramatically reduce the amount of labeled data required to achieve high performance. This approach is particularly useful in situations where labeling data is expensive or time-consuming, such as medical imaging or natural language annotation.

Few-Shot Learning: Learning from Few Examples

Few-shot learning is a subfield of machine learning that focuses on developing models that can learn from small data i.e., a small number of examples. This is a different approach from traditional machine learning models, which typically require many labeled examples to achieve high performance.

One of the most popular few-shot learning technique is meta-learning or learning to learn. In this approach, a model is trained on a variety of tasks with limited data, learning to adapt quickly to new tasks with few examples. For instance, in image classification tasks, a meta-learner may be trained on several small datasets, each containing images of different objects. When presented with a new object classification task with few examples, the meta-learner can quickly adapt its knowledge to perform well on the new task.

Another few-shot learning technique is memory-augmented neural networks, which incorporate external memory to store and retrieve information about previously seen examples. This allows the model to leverage prior knowledge when encountering new tasks with limited data.

Data Augmentation: Expanding the Dataset

Data augmentation is a technique used to artificially expand the size of a dataset by creating new, slightly modified versions of existing data points. This can be achieved by applying various transformations to the original data, such as rotation, scaling, flipping, or adding noise. For example, in image classification tasks, data augmentation can involve flipping images horizontally, rotating them, or applying random crops. In NLP tasks, data augmentation techniques can include synonym replacement, word reordering, or sentence shuffling. By increasing the diversity of the training data, data augmentation can help improve the model’s generalization capabilities and performance, especially when the original dataset is small.

Machine Learning Pruning

Machine learning pruning techniques are an essential set of methods that improve data efficiency and model performance by simplifying the underlying ML architecture. These techniques remove unnecessary or redundant components, such as neurons, weights, or layers, from a model without significantly affecting its predictive capabilities. The primary goal of pruning is to reduce the complexity of the model, which can lead to faster training, reduced memory requirements, and improved generalization.

Two common pruning techniques include weight pruning and neuron pruning. Weight pruning involves eliminating the least important connections between neurons by setting their weights to zero. For example, this can be done by thresholding the weights based on their magnitude. Neuron pruning, on the other hand, involves the removal of entire neurons or even entire layers from the model. This is typically done by analyzing the activation patterns of neurons and identifying those that contribute the least to the model’s output.

Pruning techniques help in data efficiency by reducing the model’s capacity, which, in turn, reduces the amount of data needed to train the model effectively. Moreover, a pruned model is less prone to overfitting and can generalize better to new, unseen data. Additionally, it can lead to faster inference times, making it suitable for deployment on resource-constrained devices, such as mobile phones or embedded systems.

Overall, data efficient machine learning techniques are crucial for situations where obtaining large datasets is challenging or expensive. Techniques like transfer learning, active learning, few-shot learning, data augmentation, and model pruning can significantly reduce the amount of data needed to achieve high performance. By leveraging these methods, you can develop robust machine learning models even when faced with limited data resources and sparse data. Integrators of machine learning solutions must understand the capabilities and limitations of these methods towards selecting the best option for their data analytics tasks.