one-shot learning
One-shot learning is a machine learning technique where a model can learn to recognize patterns from just one or a very few examples. It is a machine learning paradigm specifically designed to address the challenge of object categorization with extremely limited data. This approach contrasts sharply with traditional machine learning algorithms, which generally require large datasets to train effectively. One-shot learning is particularly relevant in fields like computer vision, where it enables models to recognize new objects or patterns based on minimal exposure.
One-shot learning has a wide range of applications, especially in scenarios where collecting extensive labeled data is impractical, expensive, or impossible. Some of its applications include:
- Object Recognition: Recognizing objects or visual patterns from a single example or a few examples[4].
- Face Recognition: Verifying identities with minimal examples, such as comparing a passport photo to a live image[1].
- Natural Language Processing: Identifying unknown words or phrases in text based on limited examples[2].
- Medical Imaging: Diagnosing conditions or identifying features in medical images with only a few examples for reference[4].
Citations:
[1] https://encord.com/blog/one-shot-learning-guide/
[2] https://serokell.io/blog/nn-and-one-shot-learning
[3] https://en.wikipedia.org/wiki/Few-shot_learning
[5] https://en.wikipedia.org/wiki/One-shot_learning_%28computer_vision%29
[6] https://bdtechtalks.com/2020/08/12/what-is-one-shot-learning/
[7] https://www.larksuite.com/en_us/topics/ai-glossary/one-shot-learning
[8] https://paperswithcode.com/task/one-shot-learning