What is the role of domain knowledge in zero-shot learning?

In zero-shot learning (ZSL), domain knowledge provides the foundational structure that enables models to generalize to unseen classes. ZSL tasks require models to recognize or classify categories they weren’t trained on by leveraging auxiliary information that describes relationships between seen and unseen classes. This auxiliary information is derived from domain knowledge, which acts as a bridge to transfer learned patterns to new scenarios. For example, if a model is trained to recognize animals like horses and tigers, domain knowledge might encode that zebras share visual traits with horses (shape, size) and tigers (stripes), allowing the model to infer zebras without explicit training data. Without such knowledge, the model would lack the context to connect unseen classes to existing ones.

A common way domain knowledge is applied in ZSL is through semantic embeddings or attribute-based representations. For instance, the Animals with Attributes (AWA) dataset defines classes using visual characteristics like “stripes,” “furry,” or “has hooves.” A model trained on labeled images of known animals can learn to associate these attributes with visual patterns. When encountering an unseen class like a zebra, the model uses the provided attribute descriptions (e.g., “has stripes, horse-like”) to map its features to the correct label. Similarly, in natural language processing, models might use word embeddings (e.g., from Word2Vec) to relate unseen words to known ones based on semantic similarity. For example, if a model understands “cat” and “dog” through training, embeddings can help it infer that a “lynx” is closer to a “cat” in semantic space.

From a development perspective, integrating domain knowledge into ZSL requires careful design. The quality and relevance of the domain knowledge directly impact performance. For example, if attributes are too vague or overlap ambiguously (e.g., “has legs” for both birds and mammals), the model may struggle to distinguish classes. Developers often use pre-trained semantic models (e.g., CLIP for vision-language alignment) or structured knowledge bases (e.g., WordNet hierarchies) to inject domain knowledge. However, challenges like domain shift—where the knowledge source doesn’t align with the target data distribution—can arise. For instance, a model trained on synthetic animal attributes might fail on real-world images if the attribute definitions don’t account for lighting or pose variations. To mitigate this, developers might combine multiple knowledge sources or fine-tune embeddings on task-specific data to improve alignment.