0-3学习指南笔记
    英文回答：
    0-3 Learning Guide Notes.
    Core Concepts:
    Zero-Shot Learning (ZSL): A machine learning task where a model is trained on a set of classes (seen classes) and tested on a disjoint set of classes (unseen classes) without any labeled data for the unseen classes.
    Few-Shot Learning (FSL): A machine learning task where a model is trained with a limited number of labeled samples (typically 1-5 per class) for the target classes.
    Meta-Learning (ML): A learning algorithm that learns to learn new tasks efficiently. In ZSL/FSL, ML algorithms are used to learn from a distribution of tasks, each with a different set of classes.
    Methods:
    ZSL:
springboot原理图解    Semantic Embedding Methods: Embed data points and class labels into a shared semantic space, where unseen class labels can be interpolated.
    Generative Methods: Generate synthetic data for unseen classes using the seen class data.
    Attribute-Based Methods: Utilize attributes or properties associated with classes to bridge the gap between seen and unseen classes.
    FSL:
    Metric-Based Methods: Learn a metric function to measure similarity between samples and predict class labels.
    Optimization-Based Methods: Formulate the FSL problem as an optimization problem an
d solve it using gradient-based methods.
    Meta-Learning-Based Methods: Use ML algorithms to learn from a distribution of FSL tasks and improve generalization performance.
    Applications:
    Image Classification: Identify objects or scenes from unseen categories.
    Natural Language Processing: Classify text documents or translate languages using limited labeled data.

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