Definition
Zero shot Learning is a process of learning that makes it possible for machines to identify and group new objects or categories that they have never encountered or worked with before. This learning process makes use of semantic embeddings to create relationships between objects and their features, attributes, and context [1]. For instance, lets look at a scenario where a Nigerian agricultural AI system has been trained to identify cassava plant diseases from photos. One day, a farmer in Oyo State uploads an image of a maize plant with an unusual fungal infection—something the AI was never trained to recognize. Despite never seeing this specific crop disease before, the AI correctly identifies it as a fungal infection and suggests appropriate treatments. This is zero-shot learning: the AI can make intelligent predictions about situations it was never explicitly trained on, transferring its understanding of plant disease characteristics across different crops without requiring new training data for every possible scenario.
The concept of zero shot learning process in artificial intelligence [2]
Origin
The term itself first appeared in a 2009 paper by Palatucci, Hinton, Pomerleau, and Mitchell, with early foundational work done by Lampert et al. using semantic attributes to bridge the gap between seen and unseen classes.
Context and Usage
Zero shot learning is utilized across so many sectors and industries such as content management, AI and technology sector, research and academia industry, the media. The whole idea behind this learning methodology is to teach artificial intelligence models to know new objects by understanding similarities between seen and unseen classes. This assists the model to make more accurate judgements, and, like human intuition, this learning approach enables the model to adjust and learn new information.[3]
Why It Matters
The traditional machine learning paradigm relies heavily on supervised learning, where models are trained on vast amounts of meticulously labeled data. Zero-shot learning addresses these limitations by enabling models to classify unseen data without training examples. A 2023 study found that zero-shot learning models can achieve up to 90% accuracy in image classification tasks without needing labeled examples from the target classes. By learning to generalize from limited data, these models can adapt to new tasks rapidly. Imagine training a model to recognize new plant species with just a few images of each [4]. In scenarios where labeled data is unavailable, time is short, or the data landscape is unpredictable, Zero-Shot Learning shines. It empowers AI systems to extract insights from new, unstructured data sources immediately, reducing costs and accelerating deployment.[5]
Related Terms
- Few-Shot Learning: Similar to zero-shot learning, but allows the model to learn from a very small number of examples for a new class, unlike zero-shot which requires no examples at all.
- Transfer Learning: A technique where knowledge gained from training on one task is applied to improve performance on a related, but different task, which can be leveraged in zero-shot learning scenarios.
- Attribute-Based Prediction: A strategy where the model relies on descriptive attributes of unseen classes to make predictions, like describing a "zebra" as a "black and white striped horse".
In Practice
A real-life case study of zero shot learning been practiced can be seen in NVIDIA's CLIP-based Computer Vision Systems which can identify objects and concepts in images without being explicitly trained on those specific categories. In manufacturing inspection, their AI can detect new types of defects it hasn't seen during training.
References
- Birulia, D. (2023). What is Zero-Shot Learning?
- Sarojag. (2025). What is Zero Shot Learning?
- Coursera. (2025). What Is Zero-Shot Learning?
- XcubeLabs. (2024). Exploring Zero-Shot and Few-Shot Learning in Generative AI
- Dhanjal, M. (2024). What is zero-shot and few-shot learning?