Glossary

0-9

1-shot learning 2-stage detector 3D convolution 4D data 5G + AI 6DoF pose estimation 7D representation 8-bit quantization 9-layer network 0-shot learning

A

Artificial Intelligence (AI)Algorithm Attention Autoencoder AGI / Artificial General Intelligence

B

BERT Boosting Backpropagation Batch Normalization Bias

C

Classifier / Classification Cross-Validation Clustering CNN / Convolutional Neural Network Chatbot

D

Discriminative Model Deterministic Model Deep Learning Data Augmentation Deepfake

E

Epoch Encoder Ensemble Learning Explainable AI (XAI)Embedding

F

Foundation Model Forward Propagation Fusion / Multimodal Fusion Feature Extraction Fine-tuning

G

GAN / Generative Adversarial Network Grounding Generative AI Gradient Descent Graph Neural Network (GNN)

H

Hierarchical Model Hyperparameter Hallucination Heuristic Hidden Layer

I

Interpretability Instruction tuning Imbalanced Data Instance / Sample Intelligence Amplification / Augmentation

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

K-Shot Learning Kernel Trick KL Divergence (Kullback–Leibler Divergence)Knowledge Distillation K-means Clustering

L

LSTM / Long Short-Term Memory Large Language Model (LLM)Latent Variable Loss Function Learning Rate

M

Multimodal / Multimodality Multi-head Attention Machine Learning (ML)Model Meta-learning

N

NLU / Natural Language Understanding Normalization Neural Network Novelty Detection / Anomaly Detection NLP / Natural Language Processing

O

One-hot Encoding Overfitting Objective Function Optimizer Online Learning

P

Prompt Parameter Pretraining Policy / Reinforcement Learning Policy Pooling

Q

Q-learning Query Queue / Buffer Quantization Quality Estimation

R

RNN / Recurrent Neural Network Representation Learning Retrieval Augmented Generation (RAG)Reinforcement Learning (RL)Regularization

S

Softmax Sampling Supervised Learning Sequence Modeling Self-Supervised Learning

T

Tokenizer Transformer Tuning / Hyperparameter Tuning Transfer Learning Training Data

U

U-Net Uncertainty Estimation Underfitting Universal Approximation Theorem Unsupervised Learning

V

Vision Transformer (ViT)Variational Autoencoder (VAE)Vector Embedding Vanishing / Exploding Gradient Validation Set

W

Weight Decay Word Embedding Workflow Whitening / Whitening Transformation Weak Supervision

X

X-axis / feature axis XLM XLNet XAI / Explainable AI XOR problem

Y

Yoga of AI Y-transform / YUV Yield (model yield / throughput)Y-axis / feature axis YAGNI (You Aren't Gonna Need It)

Z

Z-score Normalization Zero-gradient phenomenon Zero-shot Learning / Zero-shot inference Zero-centric / Zero-bias initialization Zygosity in augmentation

什么是零样本学习 / 零样本推理

零样本学习（Zero-shot Learning，ZSL）是一种机器学习方法，可以让模型在没有直接训练样本的情况下进行推理。这种方法特别适用于需要处理新类别的场景，比如图像识别。零样本学习的核心在于利用已知类别的特征或属性来推断未知类别的特征。例如，模型可以通过理解“有翅膀的动物”来识别“鸟”这一新类别，即使它从未见过鸟的图像。

在实际应用中，零样本学习广泛应用于自然语言处理、计算机视觉和推荐系统等领域。通过使用属性描述或语义嵌入，模型能够理解并推断新类别的性质。在图像分类中，模型可以通过理解“有翅膀的动物”来识别“鸟”，即使它从未见过鸟的图像。

零样本推理是指在推理过程中应用零样本学习能力。这种能力在许多应用场景中至关重要，尤其是在数据稀缺或新兴领域，如自动驾驶、机器人技术和个性化推荐等。

这种技术的优势在于提高了模型的泛化能力和灵活性，减少了对大量标注数据的依赖。然而，它也存在一定的挑战，例如如何准确地定义类别之间的关系、如何处理噪声属性等。

未来，随着人工智能和深度学习技术的发展，零样本学习和零样本推理有望在更多领域得到广泛应用，推动智能系统的自主学习能力。