When machine-learning data are scarce, first identify what is actually missing: examples, labels, coverage of important cases, or time from expert annotators. The right response depends on that gap. Transfer learning is often a practical first experiment; active learning can focus costly labeling; and augmentation or unlabelled data can help when their assumptions hold. None removes the need for a trustworthy, held-out evaluation set.
1. Make data collection and labeling more efficient
“Not enough data” can describe several different problems. You may have too few raw examples, many examples but few labels, too few examples of rare classes, or labels that require expensive expert judgment. Diagnose which constraint is limiting the model before collecting more data indiscriminately.
When annotation is costly, active learning can help prioritize which examples a person should label. Rather than selecting examples at random, the model or a selection procedure identifies items expected to be useful for improving it. This can make human review more focused, but the selection process can also overrepresent familiar or uncertain cases and miss parts of the real operating distribution. Keep annotation rules consistent and check performance on a separate, representative validation set.
A UK Defence Science and Technology Laboratory guide published on 7 December 2020 describes the issue directly: “Sometimes state-of-the-art machine learning models cannot be applied due to a lack of data or the expense and time required to label enough examples.”
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2. Transfer learning from a pretrained or related model
Transfer learning starts with a model that has already learned useful representations from a larger or related dataset, then adapts it to the target task using the labels you have. Depending on the model and task, you may freeze some layers while training others, or fine-tune more of the network.
This is often the fastest baseline to try when a suitable pretrained model exists. Its advantage depends on whether the source model learned features that matter for the target data. Differences in domain, image conditions, language, population, or task can weaken the benefit; in some cases, transferring the wrong representations can hurt. Evaluate on target-domain examples rather than assuming that success on the source task will carry over.
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3. Augment or synthesize examples with quality controls
Data augmentation creates additional training examples by transforming existing ones. The key test is whether a transformation preserves the correct label for the task. A flip may be valid for one image-classification problem but invalid for another where orientation changes the meaning. Text augmentation includes token- and sentence-level changes, adversarial methods, and transformations in a model’s hidden representation; each still needs to be checked against the task’s labeling rules.
Generative methods can produce synthetic examples, but more examples do not automatically mean more useful information. Generated or transformed samples can contain artifacts, incorrect labels, or patterns that reproduce and amplify bias in the original data. Review quality and label validity before relying on these examples, and do not let synthetic data replace an independent evaluation set.
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4. Learn from unlabelled data
If you have many raw examples but few annotations, self-supervised or semi-supervised learning can use the unlabelled portion to build useful representations or improve a model. A common approach is to pretrain or adapt using unlabelled data, then fine-tune with the trusted labeled subset.
Pseudo-labeling is another option: a model assigns provisional labels to unlabelled examples, and selected predictions are used for further training. Confidence controls can reduce the number of uncertain predictions admitted, but confidence alone does not guarantee correctness. Errors can reinforce themselves, especially when the unlabelled examples differ from the labeled set. Keep evaluation data untouched during training and examine results across relevant subgroups or operating conditions.
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5. Consider few-shot, zero-shot, or meta-learning
Few-shot and zero-shot approaches use prior representations or task experience to make predictions or adapt with very few labeled examples. Meta-learning goes further by training an adaptation strategy across multiple tasks, with the aim of learning how to learn a new task quickly.
These methods are most credible when the new task resembles the tasks or data that informed the model’s prior knowledge. A small or biased set of support examples can produce fragile results, and sophistication does not guarantee an advantage under domain shift. Compare against a straightforward pretrained-model baseline and evaluate on examples that represent the intended use.
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How to choose among the five methods
Compare the options against the constraints of your project rather than treating them as interchangeable ways to manufacture data.
| Method | Best fit | Main cost or risk |
|---|---|---|
| Active learning and better labeling | Labels are expensive and a human can review selected examples | Selection bias or inconsistent annotation |
| Transfer learning | A useful pretrained model from a related domain or task is available | Domain shift or negative transfer |
| Augmentation or synthesis | Transformations preserve labels, or generated data can be validated | Artifacts, label corruption, or bias amplification |
| Self-supervised or semi-supervised learning | Many relevant unlabelled examples are available | Confirmation bias in pseudo-labels or evaluation leakage |
| Few-shot or meta-learning | New tasks resemble prior tasks or available representations | Fragile generalization outside the training task family |
Before committing, check six things: the cost of obtaining labels; similarity between source and target domains; the amount and quality of unlabelled data; compute and latency limits; likely distribution shift; and whether synthetic or pseudo-labeled examples can be validated.
Quick Recap
- Set aside a small, trusted validation set that is not used to train or select pseudo-labels.
- Try transfer learning when a relevant pretrained model is available, and measure its performance on target-domain examples.
- If labels remain the bottleneck, use active learning to focus annotation effort and maintain consistent labeling rules.
- Add augmentation or unlabelled-data objectives only when their examples and assumptions can be checked.
- Use few-shot or meta-learning when relevant prior task experience exists, and compare it with a simpler baseline.
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