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Few-Shot vs Zero-Shot Learning: A Beginner’s Guide with Examples and Tools

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In today’s data-driven world, efficiently training machine learning models can be a game-changer, especially when labeled data is scarce. This article provides a clear guide on Few-Shot and Zero-Shot Learning—techniques that empower models to operate effectively even with limited data. Whether you’re a data scientist, a machine learning enthusiast, or a business professional, you will gain insights on how to implement these approaches, their differences, and practical examples to enhance your learning experience.

Core Concepts and Definitions

Formal definitions help clarify the distinctions:

  • N-way K-shot: A task involving N classes with K labeled examples per class in the support set, evaluated on a separate query set containing instances for classification.
  • Support set vs. query set: The support set consists of a few labeled examples used for adaptation or prototype formation, while the query set contains validation items to measure performance.
  • Episodic evaluation: Few-shot learning models are assessed through episodes—groups of N-way K-shot tasks sampled from datasets—to mimic the adaptation process and reduce overfitting.

Zero-shot learning operates by mapping inputs and class descriptions into a shared semantic space, using similarity comparisons for label inference. Semantic representations may include attribute vectors, word embeddings (like word2vec and GloVe), or contextual embeddings such as BERT and CLIP.

Key Differences:

  • Few-shot Learning: Requires K labeled examples for each target class; it adapts rapidly and relies on a few examples.
  • Zero-shot Learning: No labeled examples are needed for target classes, although it needs auxiliary semantic information, like text descriptions or attributes.

Variants and Middle Grounds:

  • One-shot learning: K = 1 (a specific type of few-shot learning).
  • Generalized zero-shot learning (GZSL): The system must predict both seen and unseen classes simultaneously, providing a more realistic and challenging scenario.

Main Approaches and Techniques

There are various methods used in few-shot and zero-shot learning, each presenting unique assumptions, strengths, and challenges. Here are the key techniques:

1. Metric-based Approaches

These methods create an embedding space where similar class examples are grouped closely. For instance:

  • Prototypical Networks: Calculate a class prototype by averaging the embeddings of support examples and classify queries based on proximity to prototypes.
  • Matching Networks: Use attention mechanisms over support examples for label predictions.

Pros: Simple and fast adaptation; strong benchmarks in few-shot vision tasks.

2. Optimization / Gradient-based Meta-learning

Training the model for quick adaptability with gradients:

  • MAML (Model-Agnostic Meta-Learning): Optimizes initial parameters so that a few gradient steps on a small support set yield well-performing task-specific models.

Pros: Flexible and appealing; Cons: More complex and heavier on computation.

3. Transfer Learning and Fine-Tuning

Utilizing strong pretrained models (like ResNet, ViT, BERT, and RoBERTa) and either fine-tuning the head or using efficient adaptations (like adapters and LoRA) on smaller datasets yield strong performance.

4. Prompting and In-Context Learning (for Large Language Models)

Large language models (LLMs) like GPT-3 can execute few-shot tasks through labeled examples in the prompt, requiring no parameter updates. This capacity was showcased in the paper “Language Models are Few-Shot Learners” here.

5. Semantic-Embedding Approaches for Zero-shot Learning

These approaches map inputs and labels into a shared semantic space for similarity scoring. Examples include:

  • Attribute-based methods: Utilizing hand-crafted or learned attributes related to each class, suitable for fine-grained tasks.
  • Word/Text embeddings: Employing pretrained text representations for class names or descriptions.

Contrastive pretrained models, like CLIP, learn joint image and text encoders, excelling in zero-shot classification by comparing images to textual prompts.

6. Hybrid Approaches

Combining various methods often yields the best practical results, such as initializing with a CLIP embedding space and adapting through prototypes derived from a few examples.

Notable models and frameworks include:

  • CLIP (OpenAI): A widely used contrastive image-text pretraining model for zero-shot vision.
  • GPT-family / Large Language Models: Leveraging few-shot in-context learning for tasks in NLP.
  • T5 / BART / RoBERTa: Strong pretrained models effective for zero-shot text classification.

Tools and Utilities

  • Hugging Face Transformers: Access a wide array of pretrained models and pipelines for zero-shot classification here.
  • PyTorch / TensorFlow: Custom implementations for prototypical networks or MAML.
  • Efficient Adaptation Tools: Utilize adapters and LoRA for fast adaptation.

Lightweight Experimentation

To run smaller models locally, check our guide on running small language models locally here.

Practical Examples and Recipes

Here are beginner-friendly hands-on recipes you can try using Hugging Face and small notebooks or Colab for quick iterations:

1. Zero-shot Text Classification (NLI-based)

Steps:

  1. Define candidate labels, e.g., [“bug report”, “feature request”, “praise”].
  2. Convert each label into a hypothesis template: “This text is a {label}.”
  3. Use an NLI model like BART-large-MNLI to score the strength of the text relating to each hypothesis; select the label with the highest score.

Code:

from transformers import pipeline

classifier = pipeline('zero-shot-classification', model='facebook/bart-large-mnli')
sequence = "The app crashes when I click the save button."
candidate_labels = ["bug report", "feature request", "praise"]

result = classifier(sequence, candidate_labels)
print(result)

Docs: Hugging Face Zero-shot Classification.

2. Few-shot Image Classification with CLIP + Prototypes

Recipe:

  1. Extract CLIP image embeddings for K labeled examples per class to calculate class prototypes.
  2. For each query image, compute its embedding and assign it to the closest prototype using cosine similarity.

Code Sketch:

from PIL import Image
import torch
from transformers import CLIPProcessor, CLIPModel

model = CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
processor = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32')

# support_images: dict[label] = list of PIL images
def get_embedding(image):
    inputs = processor(images=image, return_tensors='pt')
    with torch.no_grad():
        emb = model.get_image_features(**inputs)
    return emb / emb.norm(dim=-1, keepdim=True)

# Build prototypes
prototypes = {}
for label, imgs in support_images.items():
    embs = torch.vstack([get_embedding(img) for img in imgs])
    prototypes[label] = embs.mean(dim=0) / embs.mean(dim=0).norm()

# Classify query
q_emb = get_embedding(query_image)
scores = {label: (q_emb @ proto.T).item() for label, proto in prototypes.items()}
pred = max(scores, key=scores.get)

3. Prompting an LLM with Few Examples (NLP)

Steps:

  1. Select 3–10 representative input/output examples.
  2. Format uniformly in the prompt: “Input: …\nLabel: …\n---\n”.
  3. Append the new input and ask the model to predict the label.

Dataset Recommendations

  • Omniglot: For few-shot character recognition.
  • miniImageNet / tieredImageNet: Standard few-shot image benchmarks.
  • CUB (Caltech-UCSD Birds): Used in zero-shot evaluations.
  • GLUE / MNLI: NLI datasets for zero-shot text classification with entailment.

Evaluation Metrics and Benchmarks

  • Common Metrics:
    • Accuracy: or top-K accuracy for image classification tasks.
    • AUROC: for binary scoring tasks.
    • For GZSL: Report seen and unseen class accuracies along with the harmonic mean for balance.

Episodic Evaluation Protocol

Sample multiple N-way K-shot episodes from the dataset, measure accuracy per episode on the query set, then calculate the mean with a confidence interval to simulate the few-shot learning process.

Benchmark Datasets

  • miniImageNet, tieredImageNet, Omniglot: For few-shot vision.
  • CUB and variants: For zero-shot fine-grained tasks.
  • GLUE/MNLI: For NLI-based zero-shot text classification.

For standardized evaluation practices, see, “Zero-Shot Learning — A Comprehensive Evaluation” here.

Challenges, Limitations, and Common Pitfalls

  • Overfitting & High Variance: Small support sets can lead to unstable performance. Mitigation strategies include evaluating many random samplings and averaging results.
  • Pretrained Model Bias: Models inherit biases from training data. Caution is advised in sensitive applications.
  • Quality of Labels: The zero-shot approach depends on the semantic quality of inputs; phrasing is crucial, especially in prompt-based methods.
  • Mismatch in Evaluation: Benchmarks may not reflect real-world conditions; always validate in a practical environment.
  • Calibration: Confidence scores for unseen classes could be unreliable; consider calibration strategies or thresholding.

Getting Started: A Minimal Roadmap

Quick Experiment Checklist:

  1. Select a small problem and dataset (e.g., categorizing emails or classifying animal images).
  2. Execute a zero-shot baseline using Hugging Face pipelines (fast and informative) - visit here.
  3. If zero-shot isn’t sufficient, extract embeddings from pretrained models (like CLIP for images) to develop a prototype-based classifier.
  4. For enhanced accuracy, consider fine-tuning a small head or utilizing adapters/LoRA.
  5. Document experiments (using Weights & Biases or MLflow) and log prompts, seeds, and dataset splits.

Resources and Tutorials:

  • Hugging Face Notebooks for Zero-shot and CLIP.
  • Colab demonstrations of prototypical networks and CLIP.
  • Our guide on running smaller models locally here.
  • Foundation Models: Larger pretrained models will enhance zero/few-shot learning but incite discussions on cost, privacy, and ethics.
  • Multimodal Semantics: Combining richer text and visual context can expand zero-shot capabilities across different tasks.
  • Efficient Adaptation: Parameter-efficient strategies (like LoRA) will facilitate few-shot fine-tuning with limited resources.
  • Responsible Deployment: Improvements in interpretability, bias mitigation, and robust evaluation are essential as these techniques move towards production.

Conclusion and Next Steps

Few-shot and zero-shot learning present valuable methods for tackling challenges posed by limited labeled data. While zero-shot is suitable for quick iterations with extensive label sets, few-shot learning typically offers greater accuracy with a limited number of examples.

Actionable Next Steps:

  • Quickly run a zero-shot baseline with the Hugging Face pipeline (linked above).
  • Experiment with a CLIP-based prototype approach for images or a prototypical network for an effective few-shot baseline.
  • Experiment locally using our guide for smaller models here.

Further Reading and References:

  • Brown, T., et al. (2020). Language Models are Few-Shot Learners. Link
  • Hugging Face — Zero-shot classification pipeline docs. Link
  • Xian, Y., et al. (2018). Zero-Shot Learning — A Comprehensive Evaluation. Link

If you develop an interesting project, consider sharing it as a guest post here. Happy experimenting!

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