query set
The query set in few-shot learning contains the test examples used to evaluate model performance after the model has been given the labeled support set. It serves as the episode's internal test set — measuring how well the model learned from the few provided examples.
Role in a Few-Shot Episode
- Support Set: The K labeled examples per class that the model uses to "learn" the task. Analogous to training data.
- Query Set: Additional examples from the same N classes that the model must classify. Analogous to test data.
- Evaluation: Predictions on query examples are compared against true labels to compute episode accuracy.
Example: 5-Way 5-Shot Episode
| Component | Content | Purpose |
|---|---|---|
| Support Set | 5 classes × 5 examples = 25 labeled images | "Learn" these classes |
| Query Set | 5 classes × 15 examples = 75 unlabeled images | Classify using support knowledge |
| Output | Accuracy on query predictions | Evaluate few-shot performance |
Query Set in Meta-Training vs. Meta-Testing
- During Meta-Training: Query set loss drives model parameter updates. The model learns to perform well on queries after seeing only the support set. Gradients flow through both support processing and query classification.
- During Meta-Testing: Query set provides the final evaluation metric. No parameter updates — this is the true test of few-shot generalization.
Key Properties
- Disjoint from Support: Query and support sets must be completely non-overlapping — the same example cannot appear in both. This ensures unbiased evaluation of generalization.
- Same Classes: Query examples come from the same N classes as the support set — the model must classify queries into one of the N support classes.
- Typical Size: Usually 10–20 query examples per class, though this varies by benchmark. More queries provide more stable accuracy estimates.
Query Set in Different Methods
- Prototypical Networks: Compute class prototypes from support set, then classify each query by nearest prototype using Euclidean distance.
- MAML: Adapt model parameters using support set gradients, then evaluate adapted model on queries.
- Matching Networks: Each query attends to all support examples via learned similarity, producing a weighted classification.
Inductive vs. Transductive Processing
- Inductive: Each query example is classified independently — no information flows between query examples.
- Transductive: All query examples are processed jointly — the model can use the distribution and structure of the query set to improve predictions. Typically improves accuracy by 2–5%.
Impact on Evaluation
- Episode Accuracy: Fraction of correctly classified query examples within a single episode.
- Reported Accuracy: Average accuracy across hundreds or thousands of test episodes, typically reported with 95% confidence intervals.
- Query Count Sensitivity: More query examples per class provide more stable accuracy estimates but increase computational cost per episode.
The query set is the measurement instrument of few-shot learning — it reveals how effectively the model has learned to generalize from the few support examples to new instances of the same classes.