Zero-Shot Learning
What Is Zero-Shot Learning?
Zero-Shot Learning (ZSL) is a cutting-edge artificial intelligence technique that allows models to identify and categorize new objects or classes without any prior labeled examples of those classes. Instead of relying on extensive training data, ZSL uses semantic information-such as textual descriptions or attribute relationships-to make accurate predictions about unseen categories.
Analyze Your Use Case
NYRIS integrates Zero-Shot Learning into its visual search technology, enabling instant recognition of rare, new, or unique products for clients in manufacturing, retail, and e-commerce.
How Does Zero-Shot Learning Work?
- Semantic Embedding:
The system encodes both visual data (images) and semantic information (such as product descriptions or attributes) into a shared feature space using deep learning algorithms. - Attribute Mapping:
When a new, unseen object is encountered, the model compares its features to the semantic attributes or textual descriptions, identifying similarities and relationships. - Generalization and Prediction:
The AI leverages its understanding of attribute relationships to accurately classify or identify objects-even if it has never seen a direct example-enabling rapid onboarding of new products or categories. NYRIS applies this process to deliver sub-second search results across massive product databases.
Use Cases
- Manufacturing (Spare Parts Identification)
Zero-Shot Learning enables instant recognition of rare or newly designed spare parts, reducing machine downtime and streamlining maintenance. NYRIS’s technology is trusted by manufacturers like DMG Mori and Trumpf. - E-commerce (Product Discovery)
Customers can search for products using descriptions or images, even for items not previously included in the catalog. NYRIS powers visual search for leading retailers, improving product discovery and customer satisfaction. - Retail (Inventory Management)
Store teams can identify and manage new or unique inventory items without manual data entry, increasing efficiency and reducing errors. NYRIS’s solutions are in use by partners such as IKEA and METRO.
Benefits For Your Company
- Rapid Integration of New Products
Instantly recognize and classify new items without the need for extensive labeled data, accelerating time-to-market and reducing manual onboarding. - Superior Recognition Accuracy
Achieve up to 99.7% accuracy in identifying products, even for unseen or rare categories, ensuring reliable operations and customer experiences. - Scalable, Real-Time Performance
Process over 500 million products in under a second, supporting enterprise-scale databases and high-traffic environments.
FAQs
Can Zero-Shot Learning handle complex industrial parts?
Yes. NYRIS’s visual search engine, powered by Zero-Shot Learning, accurately identifies complex parts by leveraging attribute-based reasoning and semantic mapping.
How does Zero-Shot Learning differ from traditional machine learning?
Traditional models require thousands of labeled examples for each class, while Zero-Shot Learning can recognize new classes using semantic relationships, making it ideal for dynamic or rapidly changing catalogs. NYRIS uses ZSL to quickly onboard new products for clients.
Is Zero-Shot Learning scalable for large databases?
Absolutely. NYRIS’s platform delivers sub-second search results across more than 500 million products, ensuring scalability and speed for enterprise customers.
About NYRIS
Founded in 2015 by Anna and Markus Lukasson-Herzig, NYRIS is a pioneer in visual search technology and AI-powered image recognition. With €10 million in recent funding from Trumpf Venture, the European Innovation Council, and investors like IKEA, NYRIS operates from Berlin and Düsseldorf, serving industry leaders in manufacturing, retail, and e-commerce. NYRIS’s expertise in Zero-Shot Learning, synthetic data generation, and seamless enterprise integration makes it a trusted partner for companies seeking state-of-the-art visual search solutions.