Zero-shot learning (ZSL) is an approach in machine learning that allows models to make predictions on classes they have never encountered during training. Instead of relying solely on labeled training data for every class, ZSL uses rich semantic information, such as attributes or descriptions, to understand relationships between known and unseen classes. When it comes to adversarial examples—inputs that have been intentionally modified to deceive the model—zero-shot learning can address these challenges by leveraging its ability to generalize from the attributes of known classes.
One way zero-shot learning deals with adversarial examples is through its inherent focus on semantic information. By using attributes or descriptions linked to each class, the model can gain a better understanding of what constitutes a valid example of a class. For instance, if a model was trained with attributes like "striped" or "furry" to identify animals, it might still correctly identify a dog even if presented with a modified image that seems adversarial. By relying on these attributes, the model focuses on key features rather than solely on pixel values, making it somewhat resilient to straightforward attacks.
However, this does not mean zero-shot learning is immune to adversarial examples. Adversarial attacks can still manipulate semantic features or exploit the relationships between visible and unseen classes. For example, if an adversarial example has similarities to known attributes but is fundamentally different in a manner not considered by the model, it could lead the model to make incorrect predictions. Therefore, while ZSL offers a framework for dealing with adversarial examples through its attribute-based understanding, continual advancements and defenses against evolving adversarial techniques are necessary to maintain its robustness in practical applications.