Transductive learning in few-shot learning allows the model to leverage information about the structure of the entire query (test) set during prediction, rather than classifying each query example independently. It exploits the distributional properties of the test batch for improved accuracy.

Inductive vs. Transductive

• Inductive: Process each query example independently — prediction for one query doesn't depend on other queries. Standard approach.
• Transductive: Process all query examples jointly — the model can use relationships, clusters, and distributions within the query batch to inform predictions.

Why Transductive Helps

• Cluster Structure: Query examples from the same class tend to cluster in feature space. The model can identify these clusters even without labels.
• Distribution Information: The query set reveals the marginal distribution of test data — useful for calibrating decision boundaries.
• Mutual Information: One query example's classification can inform others — if two queries are very similar, they likely share a class.
• Typical Accuracy Improvement: 2–5% over inductive methods on standard benchmarks.

Transductive Approaches

• Label Propagation: Construct a graph connecting support and query examples by feature similarity. Propagate labels from support nodes to query nodes through the graph using iterative message passing.
• Transductive Fine-Tuning: Adapt model parameters using both labeled support AND unlabeled query examples. Use entropy minimization on query predictions as an unsupervised signal.
• Sinkhorn-Based Methods: Enforce balanced class assignments across the query set — if there are 5 classes and 75 queries, encourage roughly 15 assignments per class using the Sinkhorn-Knopp algorithm.
• Expectation-Maximization (EM): Iteratively assign soft labels to query examples (E-step) and update class representations (M-step) — alternating until convergence.
• Transductive Prototype Refinement: Start with prototypes from support examples, then iteratively refine prototypes using high-confidence query assignments.

Graph-Based Methods

• GNN for Few-Shot: Build a graph with support and query examples as nodes. Use Graph Neural Networks to propagate information — node features are updated based on neighbors, allowing label information to flow from support to query nodes.
• Edge-Labeling GNNs: Predict edge labels (same-class or different-class) for all pairs of nodes in the graph.

Assumptions and Limitations

• Batch Availability: Requires access to the full query batch at once — doesn't work for streaming/online scenarios where examples arrive one at a time.
• Class Coverage: Assumes query set contains examples from all support classes — if a class is absent from the query batch, methods like Sinkhorn can malfunction.
• Equal Representation: Some methods assume roughly equal class distribution in queries — violated in imbalanced test scenarios.
• Computational Cost: Joint processing of all queries is more expensive than independent classification.

Transductive learning is a powerful technique for few-shot learning when the full test batch is available — it extracts additional signal from the unlabeled test data that purely inductive methods waste.