Can zero-shot learning be used for anomaly detection?

Yes, zero-shot learning (ZSL) can be used for anomaly detection. ZSL is a machine learning approach where a model performs tasks without explicit training on specific examples. Instead, it leverages prior knowledge or auxiliary information (like textual descriptions or semantic relationships) to generalize to unseen classes. In anomaly detection, this means identifying unusual patterns or outliers that differ significantly from normal data, even if those anomalies were not part of the training data. By using semantic or contextual cues, ZSL models can infer what constitutes an anomaly without requiring labeled anomaly examples, making it useful in scenarios where anomalies are rare, undefined, or constantly evolving.

For example, consider network security: a ZSL model could detect novel cyberattacks by understanding descriptions of attack patterns (e.g., “unauthorized data exfiltration”) instead of relying solely on historical attack data. Similarly, in manufacturing, a model trained on normal sensor readings could identify defective products by referencing textual definitions of defects (e.g., “temperature spikes beyond X degrees”). These models often use techniques like embedding semantic features into a shared space, where similarities between observed data and anomaly descriptions are measured. Pre-trained language-vision models like CLIP or BERT can be adapted here, mapping data features to semantic concepts (e.g., “unusual vibration”) to flag deviations.

However, challenges exist. ZSL requires high-quality auxiliary information—poor descriptions or mismatched semantics can lead to false positives. For instance, a medical imaging system using ZSL might misinterpret a rare but benign anomaly as critical if the semantic descriptions are vague. Additionally, computational costs for comparing data against numerous anomaly descriptors can be high. Hybrid approaches, combining ZSL with traditional methods like autoencoders (to model normal data) or one-class classifiers, can mitigate these issues. For developers, tools like Hugging Face’s transformers or OpenAI’s CLIP API provide accessible starting points. While ZSL isn’t a universal solution, it offers a flexible way to detect novel anomalies when labeled data is scarce or dynamic.