Selbstüberwachtes Lernen
What Is Self-Supervised Learning?
Self-supervised learning is a machine learning approach where algorithms train themselves by creating labels from raw, unlabeled data-allowing the model to learn patterns and features without extensive human intervention. This method is especially valuable for visual search technology, as it enables AI systems to improve accuracy and adaptability with less labeled data.
Analysieren Sie Ihren Anwendungsfall
NYRIS uses self-supervised learning to accelerate image recognition and synthetic data generation, making AI solutions more scalable and cost-effective.
How Does Self-Supervised Learning Work?
- Data Preparation: The model uses large volumes of unlabeled data (such as images or product photos) and automatically generates pseudo-labels by predicting parts of the data from other parts.
- Representation Learning: By solving these prediction tasks, the AI learns to extract meaningful features and representations, which are essential for accurate image recognition and visual search.
- Fine-Tuning: The learned representations can be further refined with a small amount of labeled data, boosting performance on specific industry tasks. NYRIS applies this process to train models for spare parts identification and product discovery.
Anwendungsfälle
- Manufacturing: NYRIS uses self-supervised learning to recognize spare parts from limited labeled images, reducing machine downtime by up to 30%.
- E-commerce: Enables rapid product matching and personalized recommendations by training on vast catalogs with minimal manual labeling.
- Retail: Improves inventory management by identifying new products and variants using self-supervised models, keeping stock data accurate and up to date.
Vorteile für Ihr Unternehmen
- Lower Annotation Costs: Reduces manual labeling effort by up to 85%, saving time and resources.
- Higher Accuracy: Achieves recognition rates above 99.7% by leveraging large, diverse datasets for training.
- Faster AI Deployment: Accelerates the rollout of AI-powered solutions by minimizing the need for labeled data and enabling rapid adaptation to new products or categories.
FAQ
How does self-supervised learning reduce data labeling costs?
By generating its own training labels from raw data, self-supervised learning drastically cuts the need for manual annotation, as demonstrated in NYRIS’s visual search projects.
Can self-supervised learning handle new product types or categories?
Yes, NYRIS’s models adapt quickly to new products by learning from available unlabeled images, ensuring up-to-date recognition in dynamic environments.
Is self-supervised learning suitable for all industries?
While especially effective in manufacturing, e-commerce, and retail, self-supervised learning can be applied wherever large amounts of unlabeled data are available.
Über NYRIS
Founded in 2015, NYRIS is a leader in visual search and AI solutions, with headquarters in Berlin and Düsseldorf. Backed by €10 million in funding from investors like IKEA and Trumpf Venture, NYRIS serves global leaders in manufacturing, automotive, retail, and e-commerce. The company’s expertise in self-supervised learning, synthetic data generation, and deep learning ensures fast, accurate, and scalable AI-powered product identification and search.