Exemplar selection is the process of choosing which specific examples to store in a limited memory buffer for continual learning. Since buffer space is constrained, selecting the most informative, representative, or useful examples is critical for maximizing knowledge retention with minimal storage.

Selection Strategies

• Random Selection: Choose examples uniformly at random. Surprisingly effective and serves as a strong baseline.
• Herding (iCaRL): Select examples whose feature-space mean best approximates the overall class mean. Greedily picks the example that minimizes the distance between the buffer mean and the true class mean.
• K-Center Coreset: Select examples that maximize coverage of the feature space — each selected example should represent a different region of the data distribution.
• Entropy-Based: Select examples where the model is most uncertain (high entropy in predictions). These boundary examples are often most informative.
• Gradient-Based: Select examples whose gradients are most representative of the overall gradient direction for the task.
• Diversity Maximization: Select examples that are maximally different from each other, ensuring broad coverage.
• Reservoir Sampling: Maintain a statistically uniform sample without needing to see all data at once — ideal for streaming settings.

Evaluation Criteria

• Representativeness: Do the selected examples capture the diversity and distribution of each class?
• Discriminativeness: Do the selected examples preserve decision boundaries between classes?
• Compactness: Can a small number of examples achieve performance close to replaying all data?

Task-Specific Considerations

• Class-Balanced Selection: Ensure each class has equal representation in the buffer — critical for maintaining balanced performance.
• Difficulty Balancing: Store a mix of easy (typical) and hard (boundary) examples — easy examples for maintaining core knowledge, hard examples for preserving decision boundaries.
• Temporal Diversity: For tasks with temporal patterns, select examples spanning the full time range rather than concentrating on one period.

Impact on Performance

The choice of exemplar selection strategy can affect continual learning accuracy by 3–10 percentage points over random selection, with herding and coreset methods generally performing best.

Exemplar selection is a subtle but high-impact design decision — the right selection strategy can dramatically improve knowledge retention within fixed memory constraints.