Few-shot learning enables models to learn new tasks with minimal training data, and several frameworks simplify its implementation. Three widely used tools are PyTorch-based libraries like Torchmeta and Lightning, TensorFlow with extensions like TF-Slim, and HuggingFace Transformers for adapting pretrained language models. These frameworks provide abstractions for meta-learning, efficient data handling, and integration with existing architectures, making it easier to experiment with few-shot techniques without building everything from scratch.
For PyTorch users, Torchmeta is a popular choice. It offers data loaders for standardized few-shot datasets like Mini-ImageNet and Omniglot, along with modules for meta-learning algorithms such as Model-Agnostic Meta-Learning (MAML). Developers can define custom models and use Torchmeta’s wrappers to handle episodic training, where tasks are split into support (training) and query (validation) sets. Another option is PyTorch Lightning, which streamlines training loops and integrates with libraries like Weights & Biases for tracking experiments. For example, implementing a prototypical network—a method that classifies data by comparing embeddings to class prototypes—becomes straightforward with Lightning’s modular structure.
In the TensorFlow ecosystem, TF-Slim and pretrained models from TensorFlow Hub are practical for few-shot scenarios. TF-Slim reduces boilerplate code for defining architectures like ResNet, which can be fine-tuned on small datasets. TensorFlow Hub provides reusable embeddings (e.g., BERT or image encoders) that capture general features, allowing developers to add lightweight classification heads for new tasks. For instance, you could use a pretrained image encoder from Hub, freeze its weights, and train a small neural network on top to classify new object categories with just five examples per class. This approach minimizes computational costs while leveraging transfer learning.
For NLP tasks, HuggingFace Transformers simplifies adapting language models like GPT-3 or T5 to few-shot settings. The library includes pipelines for text generation and classification that can be guided with prompts—a key technique in few-shot learning. For example, by framing a sentiment analysis task as “Is this review positive? [text] → [answer],” GPT-2 can generate answers without task-specific training. Additionally, frameworks like OpenAI’s CLIP (accessible via PyTorch) combine vision and language for zero/few-shot tasks, such as classifying images using text descriptions. These tools abstract complex training procedures, letting developers focus on prompt design and evaluation.