HOT OFFERINGSKey concepts in few-shot learning
Learning from limited data
Unlike traditional deep learning, which requires thousands or millions of labeled examples, few-shot learning allows a model to generalize from just a few instances (e.g., 1 to 10 examples per class).
Support and query sets
Few-shot learning often follows a meta-learning approach, where the dataset is structured into:
- Support set: A small number of labeled examples used for learning.
- Query set: Unlabeled examples the model must classify after learning from the support set.
N-Shot, K-Way classification
Few-shot learning tasks are often described in N-shot, K-way format:
- K-way: Number of categories to classify.
- N-shot: Number of examples per category.
For example:
- 1-shot, 5-way classification: The model learns from one example per five classes.
- 5-shot, 10-way classification: The model learns from five examples per ten classes.
Approaches to few-shot learning
1. Meta-learning (“learning to learn”)
Meta-learning teaches models how to adapt quickly by training on a variety of tasks. The model learns a general strategy that can be applied to new tasks with minimal data.
Popular algorithms:
- MAML (Model-Agnostic Meta-Learning): optimizes initial parameters so the model can adapt quickly to new tasks.
- Prototypical Networks: creates class prototypes in an embedding space and classifies new samples based on their proximity.
2. Transfer learning
A model is first pretrained on a large dataset and then fine-tuned on a smaller dataset. This enables it to leverage prior knowledge.
- Example: Using a pretrained BERT model and fine-tuning it on a few medical documents.
3. Metric-based learning
Instead of direct classification, the model learns a similarity function to compare new examples against known ones.
- Siamese Networks: Compare sample pairs and determine similarity.
- Contrastive Learning: Teaches the model to differentiate between similar and dissimilar samples.
Applications of few-shot learning
Medical diagnosis
- Training AI on rare diseases where only a few cases exist.
- Detecting anomalies in medical imaging with limited labeled data.
Natural Language Processing (NLP)
- Low-resource language translation: Handling languages with few labeled examples.
- Named entity recognition in domains with limited annotated data.
Computer vision
- Facial recognition: Identifying people from just a few images.
- Object detection in rare or evolving scenarios (e.g., wildlife monitoring).
4. Robotics and reinforcement learning
Teaching robots new tasks using a few demonstrations instead of extensive training.
Few-Shot Learning vs. related concepts
| Concept | Definition | Example |
| Few-shot learning | Learns from a few labeled examples | Classifying animals from just 5 images per species |
| Zero-shot learning | Learns without any labeled examples, relying on prior knowledge | Identifying a new object class based on textual description |
| One-shot learning | Learns from just one example per class | Recognizing a person’s face from a single photo |
| Transfer learning | Uses a pretrained model and fine-tunes it on a small dataset | Training an image classifier on medical X-rays using a model trained on general images |
Challenges in few-shot learning
- Overfitting: with limited data, models can memorize instead of generalizing.
- Domain adaptation Issues: Performance may drop if the test data differs significantly from training examples.
Computational complexity: some meta-learning methods require extensive pretraining.
Conclusion
Few-shot learning enables AI to generalize from limited examples, making it valuable in fields where data collection is difficult.
By leveraging meta-learning, transfer learning, and metric-based methods, few-shot learning has become a key advancement in computer vision, NLP, and medical AI, pushing AI closer to human-like adaptability.
