Domain Adaptation
What Is Domain Adaptation?
Domain adaptation is a process in machine learning where a model trained on one dataset (the source domain) is adjusted to work effectively on a different but related dataset (the target domain). This is crucial when there are differences in data distribution, such as lighting, backgrounds, or camera types, between the training and deployment environments.
Analyze Your Use Case
NYRIS uses domain adaptation to ensure its visual search technology delivers high accuracy and reliability across diverse real-world scenarios and customer datasets.
How Does Domain Adaptation Work?
- Source Model Training: The process begins by training a machine learning model on a large, labeled source dataset, where the model learns to recognize features and patterns.
- Domain Shift Identification: The differences between the source and target domains-such as changes in image style, resolution, or environment-are analyzed. Techniques like feature alignment or adversarial training are used to minimize these differences.
- Model Adjustment and Fine-Tuning: The model is then adapted or fine-tuned using a smaller set of target domain data, allowing it to generalize and perform well on new, unseen data. NYRIS applies domain adaptation to optimize image recognition for clients with unique or evolving data environments.
Use Cases
- Manufacturing: Domain adaptation enables rapid spare parts identification even when images are captured under different factory lighting or with various camera devices. NYRIS supports global manufacturers like DMG Mori and Trumpf by ensuring consistent visual search performance across locations.
- E-commerce: Product images from different sellers or platforms often vary in quality and style. NYRIS uses domain adaptation to harmonize recognition accuracy, powering shoppable content and seamless product discovery for retailers like IKEA.
- Retail Inventory Management: Store environments and shelf layouts change frequently. Domain adaptation allows NYRIS’s solutions to maintain high accuracy in inventory checks and shelf monitoring for clients such as METRO, regardless of store conditions.
Benefits For Your Company
- Up to 85% Reduction in Manual Processes: Automate product and part identification across varying environments, reducing the need for manual intervention.
- Recognition Accuracy up to 99.7%: Achieve reliable image recognition and visual search results, even when your data sources change or evolve.
- Faster Deployment and Adaptation: Quickly roll out AI solutions to new sites or markets without extensive retraining, thanks to robust domain adaptation techniques.
FAQs
How does domain adaptation improve NYRIS’s visual search technology?
Domain adaptation allows NYRIS to deliver consistent and accurate recognition results, even when customer data varies due to different cameras, lighting, or backgrounds.
What makes NYRIS’s domain adaptation approach unique?
NYRIS combines domain adaptation with synthetic data generation and deep learning, enabling rapid customization and integration for enterprise clients across manufacturing, e-commerce, and retail.
Can domain adaptation help when data is limited in the target domain?
Yes, domain adaptation is designed to work with small amounts of target data, leveraging knowledge from larger source datasets to achieve high performance.
About NYRIS
Founded in 2015 by Anna and Markus Lukasson-Herzig, NYRIS is a leader in visual search technology and AI-driven solutions for manufacturing, e-commerce, and retail. With €10 million in funding from investors like Trumpf Venture, EIC, and IKEA, NYRIS processes over 500 million products in under a second. The company’s expertise in domain adaptation, synthetic data generation from CAD models, and strategic partnerships with industry leaders like SAP, DMG Mori, and METRO make it a pioneer in scalable, accurate image recognition.