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1-shot學習

1-shot學習是一種機器學習方法，專注於如何在僅有一個訓練樣本的情況下進行有效的分類。這種方法在數據稀缺的場景中尤其有用，例如圖像識別和自然語言處理。與傳統的深度學習模型需要大量數據進行訓練不同，1-shot學習透過利用先前的知識和遷移學習，使模型能夠從有限的樣本中學習並進行準確的預測。

1-shot學習的重要性在於它能夠顯著降低數據收集和標註的成本，特別是在醫療影像和安防監控等領域，獲取大量標註數據往往困難且昂貴。透過這種學習方法，模型能夠迅速適應新任務，減少訓練時間和資源消耗。

這種學習技術通常包括生成特徵嵌入，並使用距離度量（如歐幾里得距離或餘弦相似度）來判斷新樣本與已知樣本之間的相似性。常用的技術包括Siamese網絡、原型網絡和度量學習等。

典型的應用場景包括人臉識別、物體檢測和語音識別。例如，在人臉識別中，系統只需提供一張新面孔的圖片，就能準確識別該人。未來，1-shot學習的發展趨勢將朝著更高的準確率和更廣泛的應用領域，尤其是在即時處理和在線學習方面。

然而，1-shot學習也存在局限性，模型可能對樣本的選擇非常敏感，且在樣本質量較低的情況下，學習效果可能不佳。因此，應用時需要謹慎選擇樣本，並考慮數據的多樣性。