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

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

B

BERT Boosting Bias Backpropagation Batch Normalization

C

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

D

Deepfake Discriminative Model Data Augmentation Deep Learning Deterministic Model

E

Epoch Explainable AI (XAI)Embedding Encoder Ensemble Learning

F

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

G

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

H

Hierarchical Model Hallucination Heuristic Hyperparameter Hidden Layer

I

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

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

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

L

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

M

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

N

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

O

One-hot Encoding Optimizer Objective Function Online Learning Overfitting

P

Prompt Pooling Parameter Policy / Reinforcement Learning Policy Pretraining

Q

Q-learning Quality Estimation Query Quantization Queue / Buffer

R

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

S

Softmax Sampling Sequence Modeling Supervised Learning Self-Supervised Learning

T

Tokenizer Transformer Training Data Tuning / Hyperparameter Tuning Transfer Learning

U

U-Net Uncertainty Estimation Underfitting Unsupervised Learning Universal Approximation Theorem

V

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

W

Weight Decay Workflow Weak Supervision Whitening / Whitening Transformation Word Embedding

X

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

Y

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

Z

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

什麼是零樣本學習 / 零樣本推理

零樣本學習（Zero-shot Learning，ZSL）是一種機器學習方法，可以讓模型在沒有直接訓練樣本的情況下進行推理。這種方法特別適用於需要處理新類別的場景，比如圖像識別。零樣本學習的核心在於利用已知類別的特徵或屬性來推斷未知類別的特徵。例如，模型可以通過理解「有翅膀的動物」來識別「鳥」這一新類別，即使它從未見過鳥的圖像。

在實際應用中，零樣本學習廣泛應用於自然語言處理、計算機視覺和推薦系統等領域。通過使用屬性描述或語義嵌入，模型能夠理解並推斷新類別的性質。在圖像分類中，模型可以通過理解「有翅膀的動物」來識別「鳥」，即使它從未見過鳥的圖像。

零樣本推理是指在推理過程中應用零樣本學習能力。這種能力在許多應用場景中至關重要，尤其是在數據稀缺或新興領域，如自動駕駛、機器人技術和個性化推薦等。

這種技術的優勢在於提高了模型的泛化能力和靈活性，減少了對大量標註數據的依賴。然而，它也存在一定的挑戰，例如如何準確地定義類別之間的關係、如何處理噪聲屬性等。

未來，隨著人工智慧和深度學習技術的發展，零樣本學習和零樣本推理有望在更多領域得到廣泛應用，推動智能系統的自主學習能力。