Continual Learning Problem › Continual Learning Scenarios

Not all continual learning problems are alike. The research community has settled on three canonical scenarios that differ in what information is available during training and inference. Understanding these scenarios is important because different methods perform differently under each, and real-world applications map onto different scenarios depending on context.

The scenarios were systematically analyzed by van de Ven and Tolias (2019), who formalized the distinctions and highlighted that many papers claiming to solve continual learning were only solving the easiest scenario while presenting results in ways that obscured this. Knowing which scenario you are in determines which methods are applicable and what performance you should reasonably expect.

The three scenarios all share the same sequential training setup: the model sees tasks one at a time and cannot revisit previous data. They differ in how much task identity information is available and whether the output space changes between tasks.

In task-incremental learning, the model is given an explicit task identity at both training and test time. When you present an input for inference, you also tell the model which task it came from. This is the simplest and most permissive scenario.

Because the model always knows the current task, it can use separate output heads, one per task, and route inputs to the correct head based on the task identity. The learning challenge is therefore confined to the shared feature representation in the backbone network: can the network learn to extract useful features for all tasks without the early layers' representations being disrupted by each new task?

In natural language processing, task-incremental learning corresponds to settings like training a model sequentially on sentiment classification, then named entity recognition, then question answering, where at inference time the system always knows which of these tasks it is performing. An enterprise NLP pipeline that applies different models to different document types and knows the document type in advance is effectively task-incremental. The system selects the right task head based on the document category signal, so the only challenge is maintaining the shared encoder's quality across sequential training.

Task-IL is the least challenging scenario because the task identity signal eliminates a large source of ambiguity. The model never needs to figure out which task an input belongs to; it is told. This allows the model to maintain completely separate parameter subsets for each task's output, sidestepping the class confusion problem entirely. However, task-IL is also the least realistic in fully general settings, because real-world deployments often cannot guarantee that task identity will always be available at inference time.

The practical relevance of task-IL is highest in systems where the task is explicitly signaled by the application context. A voice assistant that knows whether it is in "navigation mode," "music mode," or "calendar mode" effectively operates in a task-IL setting. A document processing pipeline that knows whether it is handling a contract, an invoice, or a report is similar. For these cases, task-IL methods are appropriate and can achieve near-oracle performance with relatively simple techniques.

In domain-incremental learning, the task structure (the type of problem and the output space) remains the same across all tasks, but the input distribution shifts. The model must solve the same kind of problem on inputs from different distributions. The model does not receive task identity at test time.

Think of training a language model on news articles from 2020, then fine-tuning on news from 2021, then 2022. The task (next-token prediction or text classification) does not change, but the vocabulary, topics, and stylistic patterns shift as language evolves and new events occur. At inference time, you cannot tell the model which year the test document came from; it must handle all of them with a single set of parameters and a single output head.

Domain-IL is harder than task-IL because without knowing the domain, the model must develop representations that generalize across domains rather than routing to task-specific heads. There is only one output structure, and gradient updates for the new domain directly affect the weights used for the old domain. The model cannot fall back to a separate head for old inputs.

The domain shift in Domain-IL can take many forms. It might be temporal, as in the news example above, where language patterns evolve over time. It might be stylistic, such as training on academic text and then fine-tuning on social media. It might be topical, such as a medical question-answering system that must handle cardiology questions after being trained primarily on oncology. In all these cases, the input distribution changes but the output structure does not. The model must generalize across distributions that it has not seen jointly.

In the medical imaging literature, domain-IL corresponds to training a diagnostic model on images from one hospital scanner, then adapting it to a different scanner with different calibration and imaging characteristics, while maintaining accuracy on both scanner types without being told which scanner produced a given test image. This is a practically important scenario for deploying medical AI systems across healthcare institutions with heterogeneous equipment.

Class-incremental learning is the most challenging scenario. Here, the set of output classes grows over time, and the model must perform classification across all classes seen so far, without knowing which subset of classes the current input belongs to.

Consider training an image classifier first on cats and dogs (task 1), then on birds and fish (task 2), then on horses and rabbits (task 3). At test time, you present an arbitrary image and ask the model to classify it into one of all six classes. The model must remember the features of cats and dogs and correctly distinguish them from birds, fish, horses, and rabbits, even though all six classes were never seen together during training.

For language models, class-IL corresponds to training a text classifier on an expanding set of categories. You might start with spam/not-spam, then add categories for phishing, promotional, and transactional content, and the model must correctly classify any email into the right category using all labels seen so far. Each new batch of training data contains only examples from the new categories, so the model must somehow preserve its ability to recognize the old categories as well.

Class-IL is hardest for two interconnected reasons. First, the model must remember old classes without access to old data. If the only examples of cats and dogs the model ever sees come from task 1, but task 1's data is gone by the time the model encounters task 3, then the task-3 gradient updates will push the classifier weights in directions that improve fish versus birds but may simultaneously degrade cats versus dogs. Second, the model must calibrate its confidence appropriately across an expanding output space. A model trained only on the most recent classes will exhibit extreme recency bias, classifying almost everything as belonging to the most recently learned classes, because its decision boundaries have been tuned to those classes without any pressure to maintain separation from earlier classes.

The recency bias in class-IL is particularly pernicious because it is invisible within the current task's training data. If you evaluate the model only on the classes it just learned, it will look excellent. The forgetting only becomes apparent when you evaluate it on the full set of classes it is supposed to handle, including the ones from earlier tasks. This is why a proper class-IL evaluation must always test on all classes seen so far rather than only the most recent ones.

The importance of distinguishing these scenarios cannot be overstated. A method that achieves 95% accuracy in a task-IL benchmark and 45% in a class-IL benchmark on the same dataset is not a general continual learner; it works in a specific and favorable condition. Results that do not specify which scenario was used are difficult to interpret or compare, and comparisons between papers using different scenarios without acknowledging this are misleading.

When reading the continual learning literature, always check which scenario is assumed. A surprisingly large number of influential papers evaluated their methods in the task-IL setting while presenting the results as solutions to continual learning generally. Van de Ven and Tolias' 2019 survey brought this issue to wider attention and substantially improved the field's evaluation standards. Even so, you should check carefully before drawing conclusions from any continual learning paper.