Standard 7 stops to get here

Zero-Shot Learning

A model's ability to perform tasks it wasn't explicitly trained on, using only instructions or descriptions.

Your route here

7 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  3. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  4. Unsupervised Learning ✓ understood

    Learning from unlabeled data to discover hidden patterns, structures, or relationships without explicit target outputs.

  5. Self-Supervised Learning ✓ understood

    Learning representations from unlabeled data by creating supervised tasks from the data itself (masked prediction, contrastive learning).

  6. Pre-training ✓ understood

    Training a model on a large dataset (often self-supervised) before fine-tuning on specific tasks, enabling transfer learning.

  7. Transfer Learning ✓ understood

    Leveraging knowledge learned from one task/domain to improve performance on a related task with less data.

  8. Zero-Shot Learning · you are here ✓ understood

Where it sits

Before this

Transfer Learning
Zero-Shot Learning

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Language & LLMs Few-Shot Learning Learning to perform a task from a small number of examples provided in the prompt, without parameter updates. Language & LLMs In-Context Learning The ability of LLMs to learn from examples and instructions provided in the input prompt without training. Language & LLMs Instruction Following The ability of language models to understand and execute instructions provided in prompts. Vision & Multimodal CLIP Contrastive Language-Image Pre-training - a model jointly trained on images and text, enabling zero-shot image classification. Language & LLMs Prompt Engineering The practice of designing and optimizing input prompts to get desired outputs from language models. A crucial skill for effectively using LLMs.

In the research

All papers →

2 papers that build on Zero-Shot Learning .