Few-Shot Learning
Learning with Limited Data
Build AI models that learn quickly from just a few examples
no examples
one example
few examples
learn to learn
Core Few-Shot Learning Approaches
Meta-Learning Approaches
MAML (Model-Agnostic Meta-Learning)
- • Learn good initialization parameters
- • Fast adaptation to new tasks
- • Works with any model architecture
Reptile Algorithm
- • Simpler than MAML to implement
- • Uses only first-order gradients
- • Performance comparable to MAML
Meta-SGD
- • Learn learning rates for each parameter
- • Adaptive optimization strategy
- • More flexible than MAML
Metric Learning Approaches
Prototypical Networks
- • Create prototypes for each class
- • Classify based on embedding space distance
- • Simple and effective
Siamese Networks
- • Compare similarity between pairs
- • Use shared weights between networks
- • Suitable for verification tasks
Matching Networks
- • Use attention mechanisms
- • Compare with support set
- • No parameter updates needed
Transfer Learning & Pre-trained Models
Foundation Models
Large Language Models
GPT, BERT, T5 for NLP tasks
Vision Models
ResNet, Vision Transformer
Multi-modal Models
CLIP, DALL-E
Fine-tuning
Full Fine-tuning
Update all parameters
LoRA
Low-Rank Adaptation
Prompt Tuning
Tune prompts only
Domain Adaptation
Supervised
Has labels in target domain
Unsupervised
No labels in target domain
Domain Adversarial
Use adversarial training
Practical Applications
Computer Vision
Image Classification
Classify new categories with few examples
Object Detection
Detect new objects with minimal training data
Medical Imaging
Diagnose diseases from medical images
Natural Language Processing
Text Classification
Classify text with few labeled examples
Named Entity Recognition
Identify new entities in text
Machine Translation
Translate new languages with limited data
🤖 Robotics
- • Quick learning of new tasks
- • Adaptation to new environments
- • Learning from demonstrations
🛒 E-commerce
- • New product recommendations
- • Product categorization
- • Review sentiment analysis
🏦 Finance
- • New fraud pattern detection
- • Risk assessment models
- • Market prediction
Implementation Strategy
Tools & Libraries
Meta-Learning
- • learn2learn: PyTorch meta-learning library
- • Torchmeta: Few-shot datasets and utilities
- • Higher: Higher-order optimization
Transfer Learning
- • Hugging Face: Pre-trained models and fine-tuning
- • TensorFlow Hub: Ready-to-use models
- • Timm: PyTorch vision models
Development Process
Choose Approach
Meta-learning or Transfer learning
Prepare Data
Split into support and query sets
Train Model
Use episodic training
Evaluate
Test on novel classes
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