Continual Learning: How to Train Models on the Fly and Adapt to New Data

In the dynamic world of machine learning, models often need to evolve. Traditional machine learning workflows, where models are trained once and deployed, are increasingly insufficient. Data changes, new classes emerge, and user preferences shift. This is where Continual Learning steps in.

Continual learning, also known as lifelong learning, is a set of techniques that enable machine learning models to learn incrementally from a stream of data, without forgetting previously acquired knowledge. It’s about building models that can adapt and improve over time, just like humans do.

Methods in continual learning are diverse, generally falling into categories like regularization-based, architectural, and memory-based approaches. Each offers unique strengths and weaknesses, making the selection process crucial for success.

Adopting continual learning is a journey. It begins with clearly defining your objectives, moving through implementing a basic solution, and culminating in the selection and fine-tuning of the optimal continual learning method.

The key to successful continual learning lies in identifying the right goals, choosing appropriate tools, selecting a suitable model architecture, iteratively refining hyperparameters, and effectively utilizing all available data.

Early in my machine learning career, I believed the process was standardized: define the problem, gather data, train, evaluate, and deploy. Repeat for improvement.

However, real-world machine learning projects are rarely that simple. Challenges abound: limited data, restricted computing resources, and tight deadlines.

Moreover, what happens when the data distribution changes after deployment? What if a classification model needs to recognize new categories over time?

These are the concerns that keep many ML practitioners up at night. If you’re among them, continual learning offers a powerful solution.

What is Continual Learning?

Continual learning (CL) is a vibrant research area focused on creating practical methods for incremental machine learning model training.

Incremental training means the model learns from sequential batches of data, processing each sample only once as it arrives. Unlike traditional machine learning, which relies on a fixed dataset, continual learning models are fed a stream of smaller datasets over time.

Each dataset, even a single data point, is used only once. Data appears as a continuous stream, and future data is unknown.

This fundamentally changes the training paradigm. We don’t have static training, validation, and test sets in continual learning as in classic ML. While we still aim for high performance on the current data batch, a critical additional goal is to prevent catastrophic forgetting – the tendency of models to lose previously learned information when learning new things.

Key Concept:

Continual learning’s core objective is to enable models to effectively learn new concepts while preserving previously acquired knowledge.

Numerous continual learning techniques are available, applicable across various machine learning scenarios. This article focuses on continual learning for deep learning models due to their adaptability and broad applicability.

Use Cases and Applications

Before delving into methods and implementations, let’s consider: When is continual learning necessary?

Continual learning techniques are valuable when:

Models require rapid adaptation to new data: Certain ML models need frequent updates to remain effective. Consider fraud detection in financial transactions. A model with 99% accuracy on initial training data may quickly degrade as fraud patterns evolve daily. Continual learning allows the model to learn from the latest transaction data and adapt swiftly to emerging threats, ensuring ongoing protection against fraud.
Models need personalization: Imagine a document classification system serving many users, each with unique document types, vocabularies, and writing styles. Continual learning allows for personalized models by retraining after each user’s document upload. This gradual adaptation tailors the model to individual user data, enhancing classification accuracy for each user.

Model personalization via CL learning in a document classification

Generally, continual learning is essential when models operate in dynamic environments and must adapt to streaming data in real-time.

Continual Learning Scenarios

Continual learning problems can be categorized into three main scenarios based on the characteristics of the incoming data stream, each with distinct challenges and solutions.

Class Incremental Continual Learning

Class Incremental (CI) continual learning addresses situations where the number of classes in a classification task increases over time.

For example, imagine a model initially trained to classify images of cats into five breeds. Later, the requirement expands to include a sixth breed.

This scenario is common in real-world applications and is considered one of the most challenging in continual learning.

Domain Incremental Continual Learning

Domain Incremental (DI) continual learning encompasses cases where the data distribution changes over time.

Consider a model designed to extract information from invoices. If users start uploading invoices with significantly different layouts, the input data distribution shifts.

This distribution shift can degrade model performance as the new data diverges from the data the model was initially trained on. Domain incremental learning aims to maintain accuracy despite these distributional changes.

Task Incremental Continual Learning

Task Incremental (TI) continual learning is an incremental form of classic multi-task learning.

Multi-task learning involves training a single model to perform multiple tasks simultaneously. This is prevalent in Natural Language Processing (NLP), where a model might handle text classification, named entity recognition, and text summarization. Each task has a separate output layer, while the underlying model parameters are shared.

In task incremental continual learning, a single model learns to perform multiple tasks sequentially, as data for each task becomes available over time. The number of tasks may be unknown beforehand, requiring the model’s architecture to potentially expand. Each input example includes a task label to guide the model to the appropriate output. For example, classification and text summarization outputs are different, and the task label directs the model to the correct output type for the given input.

Challenges in Continual Learning

There’s no “free lunch” in machine learning, and continual learning is no exception.

Incremental model training is inherently challenging because machine learning models are prone to overfitting to new data and forgetting past knowledge. This phenomenon, known as catastrophic forgetting, remains a significant research challenge.

Class-incremental learning is particularly difficult. Learning to distinguish between an expanding set of classes is far more complex than simply adapting to data shifts. Introducing a new class can significantly alter the decision boundaries of existing classes. For example, adding a “Labrador Retriever” class to a “Dog breed classifier” can cause confusion and overlap with existing dog classes.

Task-incremental problems are relatively simpler and better understood because they can be partially addressed by freezing parts of the model (to prevent forgetting) and only training task-specific output layers.

However, regardless of the specific scenario, incremental model training is consistently more complex than traditional offline training, where all data is available upfront, allowing for techniques like hyperparameter optimization. Furthermore, different model architectures respond uniquely to incremental training. Finding the optimal (or even satisfactory) solution can be challenging, even for experienced machine learning engineers. Therefore, rigorous experimentation and careful tracking are essential. Experiment tracking platforms are invaluable for verifying ideas in practice, not just in theory.

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Continual Learning Methods

The 2010s and 2020s have witnessed rapid advancements in continual learning methods. Researchers have proposed numerous techniques to mitigate catastrophic forgetting and enhance the effectiveness of incremental model training.

These methods can be broadly categorized into architectural, regularization, and memory-based approaches.

Architectural Approaches

Architectural methods, also known as parameter-based methods, adapt the model’s structure to accommodate new data.

For example, in a task-incremental scenario where personalized text classifiers are needed for clients in different countries, a multilingual Large Language Model (LLM) can serve as the core. A different classification layer is selected based on the input text’s language. The core LLM parameters remain frozen, while the language-specific classification layers are fine-tuned with incoming data.

The core idea is to dynamically modify the model structure to preserve existing knowledge while enabling learning from new data. Model restructuring can occur as needed, such as when a new class appears or after each training batch.

Architectural approaches can be implemented by creating specialized subnetworks, as in Progressive Neural Networks, or by using multiple model heads (output layers) selected based on input data characteristics (like task labels in task-incremental scenarios).

Regularization Approaches

Regularization-based methods maintain a fixed model architecture during incremental training. To enable learning new data without forgetting old knowledge, they employ techniques such as knowledge distillation, loss function modifications, selective parameter updates, or standard regularization methods.

The overarching principle is to minimize parameter changes to prevent forgetting. These methods are generally faster and simpler to implement but often less effective than architectural or memory-based approaches, especially in challenging class-incremental scenarios. This is mainly due to their limited capacity to learn complex feature space relationships. Examples include Elastic Weights Consolidation (EWC) and Learning Without Forgetting (LwF).

The primary advantage of regularization-based methods is their ease of implementation. However, when architectural or memory-based methods are feasible, regularization techniques often serve as quick baseline solutions rather than final, high-performance solutions in complex continual learning problems.

Memory-Based Approaches

Memory-based continual learning methods involve storing a subset of past input samples (and their labels in supervised learning) in a memory buffer during training. This memory can be a database, local storage, or in-RAM storage.

The stored examples are then used in subsequent training iterations alongside new data to prevent catastrophic forgetting. For instance, a training batch might consist of both current data and randomly sampled examples from the memory buffer.

Memory-based methods are popular for tackling various continual learning problems due to their effectiveness and relative simplicity. Empirical studies have shown them to be highly effective across all three continual learning scenarios. However, they require continuous access to past data, which may not always be possible.

For example, healthcare information extraction may involve strict data retention policies, requiring the deletion of documents shortly after information extraction. In such cases, using a memory buffer is not feasible.

Another example is a robot vacuum cleaner that improves its navigation through continual learning. It captures images of its surroundings to refine its navigation model. These images often contain sensitive personal information. Model training must occur on the robot (on-device learning), and images should not be stored longer than necessary. Furthermore, storage space on the device might be limited, hindering the effectiveness of memory-based methods.

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How to Choose the Right Continual Learning Method for Your Project

Within these three categories of continual learning approaches, numerous specific techniques exist. Just as with model architectures and training paradigms, project success depends on selecting the appropriate methods. How do you determine the best approach for your specific problem?

General guidelines include:

Start with a simple regularization-based approach. If the resulting accuracy is sufficient, you have a quick and efficient solution. If not, it establishes a valuable baseline for comparison.
If even a small fraction of historical data can be stored, use a memory-based technique. This applies regardless of the model type.
Consider architectural approaches if memory-based techniques are not viable. Implementation is more complex and time-consuming, but it might be the only feasible option in certain situations.

Combinations of methods from different categories can often yield better results. Research indicates that hybrid approaches can be advantageous in many scenarios. For example, combining a memory-based method with an interchangeable output layer can effectively fine-tune personalized models for individual users.

However, identifying the right scenario and selecting a suitable method is only the first step. The next section explores practical implementation.

Adopting Continual Learning

Who Benefits from Continual Learning?

While beneficial for small companies seeking to adapt models to streaming data, continual learning is a necessity for large organizations. Managing updates for thousands of models simultaneously is simply not practical without automated continual learning processes.

Adopting continual learning in production is advantageous but challenging, especially when starting from scratch rather than adapting an existing classically trained model. Initially, without historical data, you lack the training, test, and validation sets crucial for hyperparameter tuning and model evaluation. Developing an effective continual learning solution from the ground up can be a lengthy and iterative process.

Stages of Continual Learning Development

A common and more practical approach is to transition gradually from classical training to continual learning. Chip Huyen, in her book “Designing Machine Learning Systems,” outlines four stages of advancement:

Manual, Stateless Retraining: No automation is involved. Retraining is triggered manually by developers and always involves training from scratch. Incremental training or continual learning is absent.
Automated Retraining: Models are still trained from scratch each time, but retraining is automated (e.g., using Cron schedules), and the entire pipeline (data preparation, training) is automated. While not continual learning, this establishes essential automation foundations.
Automated, Stateful Training: Models are no longer trained from scratch but fine-tuned using a subset of recent data on a fixed schedule (e.g., daily training on the previous day’s data). Basic regularization-based continual learning methods are introduced, representing a rudimentary form of continual learning.
Continual Learning: Models are trained using more advanced continual learning techniques, achieving satisfactory performance. Further training is triggered only when necessary, such as when data distribution shifts significantly or model accuracy declines.

The progression from manual retraining to full continual learning is a significant leap.

Currently, most production ML systems do not fully utilize continual learning, remaining in earlier stages of development. Reaching stage four requires a gradual evolution of existing processes. How can this be done effectively? What common mistakes should be avoided? The following section summarizes best practices for building continual learning solutions efficiently and effectively.

Top 5 Tips for Implementing Continual Learning

1. Precisely Define Your Objective

Clearly articulate your goals. Is rapid adaptation to new data paramount, even at the expense of some past knowledge? Or is preserving past knowledge the primary concern? What is the minimum acceptable level of model accuracy? These fundamental questions will guide your approach.

Architectural methods like Progressive Neural Networks excel at preserving past knowledge by freezing parameters, mitigating catastrophic forgetting. If rapid adaptation is the primary goal, simpler regularization-based methods, such as increasing weight updates for influential parameters, can be effective.

For a balance between preserving the past and learning new information, prompt tuning (an architectural approach) can be beneficial:

First, use transfer learning to create a robust backbone model. Then, during incremental training, freeze the backbone and fine-tune only a small set of additional parameters. The backbone retains past knowledge, while the extra parameters enable efficient learning of new concepts. A key advantage is that these additional parameters can be easily removed, allowing reversion to the original backbone model and baseline performance if needed.

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2. Carefully Select the Model Architecture

Deep learning models, even those seemingly similar, exhibit different behaviors under incremental training. For instance, Convolutional Neural Networks (CNNs) achieve significantly better continual learning accuracy when incorporating batch normalization and skip connections.

Furthermore, models with the same number of parameters can perform differently based on layer architecture. “Long” models have many layers with fewer parameters per layer, while “wide” models have fewer layers with more parameters per layer. Wider models tend to be better suited for continual learning than longer models. Longer models are more challenging to train effectively using backpropagation. Small weight adjustments in early layers of long models can have a magnified “snowball effect,” significantly influencing weights in later layers. Wider models are also less prone to overfitting.

3. Start Simple, Then Iterate and Improve

Initiating a continual learning project can seem daunting. Here’s a recommended roadmap:

Validate the Need for Continual Learning: Recognize that adopting continual learning is a progressive process. Assess if it truly provides tangible benefits. Avoid over-engineering; implement continual learning only if genuinely needed. For example, models requiring annual retraining might not warrant continual learning approaches.
Begin with a Naive Baseline: Implement a straightforward, basic solution first. This provides a baseline for comparison and helps avoid over-engineering when implementing more complex methods like regularization or memory buffers.
Choose the Right Method: Select a method appropriate for your problem, considering model type, data availability (past data access), and priorities (rapid adaptation vs. knowledge retention). Refer to the “How to Choose the Right Continual Learning Method” section for guidance.
Experiment Extensively: Finding the optimal solution is rarely immediate, even for experts. Experiment by simulating production-like continual learning scenarios on available data and tuning hyperparameters.
Thoroughly Understand Problems: Continual learning solutions can be fragile initially. Poor performance can stem from various factors, including uncalibrated hyperparameters, unsuitable methods, or inadequate training procedures. Prioritize understanding the root cause before implementing solutions.

4. Choose Your Tools Wisely

If you’ve decided to integrate continual learning, selecting the right tools is crucial.

Numerous methods are described in research papers, but implementing them from scratch can be time-consuming. Fortunately, high-quality libraries offer ready-to-use continual learning solutions:

Avalanche: A comprehensive PyTorch library specifically designed for continual learning research and development. It provides implementations of various CL strategies, benchmarks, and tools for experiment management.
TorchCL: Another PyTorch-based library focused on continual learning, offering a range of algorithms and functionalities.
ContinualAI Framework: A broader framework encompassing multiple CL libraries and resources, fostering collaboration and advancement in the field.

Leveraging these libraries can significantly accelerate development and experimentation.

5. Utilize Past Data If Possible

Memory-based methods are currently among the most effective for incremental training. Using memory provides significant advantages over other approaches and is relatively straightforward to implement. If even a small portion of past data is accessible for incremental training – utilize it!

In situations where past data is unavailable, before resorting to complex continual learning methods, consider if there are alternative ways to enable memory-based approaches. For example, even a memory buffer filled with artificially generated examples can be surprisingly beneficial.

Summary

Continual learning is a powerful paradigm for training machine learning models incrementally, essential for adapting to evolving data and enabling model personalization.

Achieving optimal model performance in continual learning is a journey requiring patience and iterative refinement. Remember to clearly define objectives and carefully select methods best suited to your specific use case.

As highlighted in the “Choose Your Tools Wisely” section, numerous readily available methods can enable models to learn from streaming data without forgetting past knowledge. Different methods are suited to different scenarios, so experimentation is key. These tips should help you develop effective continual learning models.

For those interested in a deeper academic understanding of continual learning, this comprehensive review paper is highly recommended.

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