Transfer Learning in Vision
Using pre-trained vision models (ImageNet) as feature extractors or fine-tuning for specific visual tasks.
Related Concepts
- Pre-training: Explore how Pre-training relates to Transfer Learning in Vision
- ImageNet: Explore how ImageNet relates to Transfer Learning in Vision
- Fine-Tuning: Explore how Fine-Tuning relates to Transfer Learning in Vision
- Feature Extraction: Explore how Feature Extraction relates to Transfer Learning in Vision
Why It Matters
Understanding Transfer Learning in Vision is crucial for anyone working with computer vision. This concept helps build a foundation for more advanced topics in AI and machine learning.
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This term is part of the comprehensive AI/ML glossary. Explore related terms to deepen your understanding of this interconnected field.
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Related Terms
Fine-Tuning
The process of further training a pre-trained model on a specific dataset to adapt it for a particular task or domain.
ImageNet
A large-scale dataset of 14M images in 20K categories, historically used as the benchmark for image classification models.
Pre-training
Training a model on a large dataset (often self-supervised) before fine-tuning on specific tasks, enabling transfer learning.