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什麼是 K-Shot Learning - AI Glossary

首頁 AI 詞彙表 AI Fundamentals什麼是 K-Shot Learning

AI 詞彙表

0-9

3D Reconstruction 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

A/B Testing Accountability Accuracy Acoustic Modeling Activation Functions Active Learning Actor-Critic Methods Actuators AdaDelta AdaGrad Adam Optimizer Adjusted R-Squared Adversarial Attacks Affordance Learning Agent-Based Modeling Agentic AI / Autonomous Agents Agentic AI Frameworks Agglomerative Clustering AI Accelerators AI Act (EU)AI Agents AI Alignment AI and Bias AI and Sustainability AI APIs AI Art Generation AI Assistants AI Audit AI Auditing AI Bill of Rights (US Blueprint)AI Containment AI Democratization AI Ethics Boards AI Ethics Guidelines AI Feature Store AI for Climate Change AI Generated Content AI Governance Frameworks AI Guardrails AI Hallucinations AI in Healthcare Ethics AI in Warfare AI Legislation AI Literacy AI Marketplaces AI Model Governance AI Model Hub AI Model Registry AI Model Weights AI Music Generation AI Orchestration AI Policy AI Regulations AI Safety AI Security AI Singularity AI Transparency Report AI Watermarking AI Winter AI Workflow Automation AI-as-a-Service Alan Turing Algorithmic Accountability Algorithmic Bias Mitigation Algorithmic Discrimination Algorithmic Transparency Andrew Ng Anomaly Detection Anomaly Detection in Security Anthropic Apache Kafka API Development API Endpoints Apriori Algorithm Artificial General Intelligence (AGI)Artificial Neural Networks Artificial Superintelligence ASICs Association Rule Learning Asynchronous Advantage Actor-Critic Attention Mechanisms AUC Audio Classification Audio Signal Processing Augmented Reality Authentication Authorization Autoencoders Automated Reasoning Automatic Speech Recognition (ASR)Automation AutoML Autonomous Navigation Autoregressive Models AGI / Artificial General Intelligence Artificial Intelligence (AI)Attention Algorithm Autoencoder

B

Bag-of-Words Model Bagging Batch Size Bayesian Inference Bayesian Networks Bayesian Optimization Bias in AI Bias-Variance Tradeoff Big Data Big Data Technologies Biometric Security BLEU Score Blockchain in AI Box Plot Byte-Pair Encoding (BPE)BERT Boosting Bias Backpropagation Batch Normalization

C

Caffe Calculus Calibration California Consumer Privacy Act (CCPA)Canary Deployment Capsule Networks Carbon Footprint of AI Case-Based Reasoning Catastrophic Forgetting Central Limit Theorem Chain-of-Thought Chinese Room Argument Class Imbalance Classification Cloud AI Platforms Cloud Computing Clustering Algorithms Code Generation Models Cognitive Architectures Cognitive Computing Cohere Colab Notebooks Collaborative Filtering Color Spaces Complex Analysis Compliance Compliance Standards (ISO IEEE)Computational Complexity Computational Fluid Dynamics Computational Theory of Mind Compute-Optimal Models Concept Drift Conceptual Graphs Conditional Probability Confusion Matrix Consciousness in AI Consistency Models Constitutional AI Constraint Satisfaction Problems Containerization Content-Based Filtering Context Window Continual Learning Continuous Integration/Continuous Deployment (CI/CD)Control Systems Conversational AI Convolutional Neural Networks COPPA Coreference Resolution Correlation Correlation Matrix Cost-Sensitive Learning Cross-Entropy Loss Curriculum Learning Cyber Threat Intelligence Cybersecurity Regulations Cross-Validation Classifier / Classification CNN / Convolutional Neural Network Chatbot Clustering

D

DALL·E Data Annotation Data Catalog Data Centers Data Cleaning Data Drift Data Governance Data Ingestion Data Integration Data Labeling Data Lake Data Lakes Data Leakage Data Lineage Data Mining Data Pipeline Data Poisoning Data Preprocessing Data Privacy Data Protection Data Protection Laws Data Quality Data Security Data Sovereignty Data Transformation Data Versioning Data Visualization Data Visualization Techniques Data Warehousing Databases for AI Davies-Bouldin Index DBSCAN Decision Boundary Visualization Decision Trees Deep Belief Networks Deep Q-Networks Deep Reinforcement Learning Deepfakes DeepMind Demis Hassabis Dependency Parsing Depth Estimation Descriptive Statistics Dialogue Systems Differential Equations Differential Evolution Differential Privacy Diffusion Models Digital Divide Digital Provenance Digital Twins Dimensionality Reduction Direct Preference Optimization (DPO)Discourse Analysis Discrete Event Simulation Discrete Mathematics Disinformation Distributed Computing Distributed File Systems Distributed Training Docker Drones Dropout Dropout Regularization Dynamical Systems Deepfake Discriminative Model Data Augmentation Deep Learning Deterministic Model

E

Early Stopping Edge AI Edge Computing Edge Detection Eigenvalues and Eigenvectors Elon Musk Embedding Size Embeddings Embodied AI Emergent Abilities Emotion Recognition Ensemble Methods Episodic Memory Ethical AI Ethical AI Guidelines Ethical Auditing Ethical Decision-Making Ethical Dilemmas Ethical Frameworks Ethics of AI ETL Processes Evolutionary Algorithms Existential Risk Expectation-Maximization Expectation-Maximization Algorithm Expected Calibration Error Expert Systems Explainability Exploration vs. Exploitation Exploratory Data Analysis Export Controls Epoch Explainable AI (XAI)Embedding Encoder Ensemble Learning

F

F1 Score Facial Recognition Fairness FastAI Feature Engineering Feature Importance Feature Selection Feature Store Feature Stores Federated Learning Fei-Fei Li Few-Shot Learning Finite Element Analysis First-Order Logic Flow Matching Force Control Foundation Model Economy Foundation Models Fourier Transform FPGAs Frame Languages Functional Analysis Forward Propagation Foundation Model Fine-tuning Feature Extraction Fusion / Multimodal Fusion

G

Game Playing AI Game Theory Game Theory Simulations Gated Recurrent Units Gaussian Mixture Models General Data Protection Regulation (GDPR)Generative Adversarial Networks Generative Models Genetic Algorithms Gensim Geoffrey Hinton Global Cooperation GPT Models Grad-CAM Gradient Boosting Machines Gradient Clipping Graph Neural Networks Graph Theory Graphics Processing Units (GPUs)Grid Search GAN / Generative Adversarial Network Grounding Graph Neural Network (GNN)Gradient Descent Generative AI

H

Hadoop Heatmap Help Heuristic Algorithms Hidden Markov Models Hierarchical Reinforcement Learning High-Performance Computing HIPAA Histogram HOG HPC Clusters Hugging Face Hugging Face Transformers Human Rights Human-in-the-Loop Human-Robot Interaction Hyperparameter Optimization Hyperparameter Tuning Heuristic Hierarchical Model Hallucination Hyperparameter Hidden Layer

I

Ilya Sutskever Image Captioning Image Classification Image Recognition Image Segmentation Impact on Employment In-Context Learning Industrial Robots Inference Inference Engines Inference Optimization Inferential Statistics Information Theory Informed Consent Infrastructure as Code Instance Segmentation Intellectual Property Rights Intelligent Agents Intrusion Detection Systems Inverse Reinforcement Learning Instruction tuning Imbalanced Data Instance / Sample Intelligence Amplification / Augmentation Interpretability

J

John McCarthy Joint Probability Distribution Juergen Schmidhuber Jupyter Notebooks JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

K-Nearest Neighbors Kai-Fu Lee Kalman Filters Keras Knowledge Cutoff Knowledge Graphs Knowledge Representation Kubernetes K-Shot Learning Kernel Trick KL Divergence (Kullback–Leibler Divergence)K-means Clustering Knowledge Distillation

L

L1 Regularization L2 Regularization Label Smoothing Language Modeling Language Models Laplace Transform Large Language Models (LLMs)Large Multimodal Models Latent Dirichlet Allocation Latent Space Law of Large Numbers Layer Normalization Learning Curve Learning Rate Decay Learning Rate Scheduling Lemmatization LIME Linear Algebra Linear Regression Log Loss Logic Programming Logistic Regression Long Short-Term Memory Networks Long-Context Models LoRA (Low-Rank Adaptation)LSTM / Long Short-Term Memory Large Language Model (LLM)Learning Rate Loss Function Latent Variable

M

Machine Consciousness Machine Translation Markov Chain Models Markov Chain Monte Carlo Markov Decision Processes Markov Models Marvin Minsky Masked Language Models Master Data Management Matplotlib Matrix Decomposition MCP Mean Absolute Error Mean Squared Error Mechanistic Interpretability Mel-Frequency Cepstral Coefficients (MFCCs)Metadata Management Microservices Midjourney Mind Uploading Mini Tool Mini-Batch Gradient Descent Mixture of Experts (MoE)MLOps Mobile Robots Model Cards Model Compression Model Deployment Model Drift Model Explainability Tools Model Monitoring Model Serving Model Stealing Momentum Optimization Monitoring and Logging Monte Carlo Methods Monte Carlo Simulations Moral Machines Motion Detection Motion Planning Multi-Armed Bandit Problem Multimodal AI Music Information Retrieval MXNet Meta-learning Multimodal / Multimodality Multi-head Attention Model Machine Learning (ML)

N

n-Grams Naive Bayes Algorithm Naive Bayes Classifier Named Entity Recognition Natural Language Generation (NLG)Natural Language Processing Natural Language Processing (NLP)Natural Language Understanding Nesterov Accelerated Gradient Network Simulations Neural Architecture Search Neural Networks Neural Processing Unit (NPU)Neuromorphic Computing Nick Bostrom NLTK Noise Reduction NoSQL Databases NumPy NVIDIA CUDA NLU / Natural Language Understanding Novelty Detection / Anomaly Detection Normalization Neural Network NLP / Natural Language Processing

O

Object Detection Object Tracking Ontologies OpenAI OpenAI GPT Optical Character Recognition Optimization Theory Out-of-Distribution (OOD) Data One-hot Encoding Optimizer Objective Function Online Learning Overfitting

P

Pandas Parallel Computing Parameter Count Parameter-Efficient Fine-Tuning (PEFT)Part-of-Speech Tagging Partial Dependence Plots Path Planning Pattern Recognition People also viewed Perception in AI Perceptron Perplexity Peter Norvig Philosophy of Mind Phonetics Pipelines Planning and Scheduling Plotly Policy Gradients Policy Optimization Pose Estimation Positional Encoding Pragmatics Precision Predictive Modeling Predictive Probability Preference Tuning Principal Component Analysis Privacy Privacy-Preserving Machine Learning Probability Density Functions Probability Theory Problem Solving Process Modeling Process-Based Supervision Prompt Chaining Prompt Engineering Prompt Injection Prompt Marketplace Prompt Templates Propositional Logic Proximal Policy Optimization Pruning PyTorch Prompt Pooling Parameter Policy / Reinforcement Learning Policy Pretraining

Q

QLoRA (Quantized Low-Rank Adaptation)Quantum Computing Quantum Machine Learning Question Answering Question Answering Systems Q-learning Quality Estimation Query Quantization Queue / Buffer

R

R-Squared Random Forests Random Search Ray Kurzweil Real Analysis Reasoning Engines Recall Recommender Systems Recurrent Neural Networks Red Teaming Regression Regression Analysis Regulatory Compliance Reinforcement Learning from Human Feedback Reinforcement Learning in Robotics Reproducibility Responsible AI Retrieval-Augmented Generation Reward Function RMSprop Robot Kinematics Robot Vision Robotic Manipulation Robotic Operating System (ROS)Robotics Transformers Robustness in AI Models ROC Curve Rodney Brooks Root Mean Squared Error Rule-Based Systems RNN / Recurrent Neural Network Reinforcement Learning (RL)Retrieval Augmented Generation (RAG)Regularization Representation Learning

S

Saliency Maps SARSA Algorithm Scalable Oversight Scaling Laws Scatter Plot Scikit-Learn SciPy Seaborn Search Algorithms Secure Hardware Secure Multi-Party Computation Secure Protocols Self-Attention Self-Driving Cars Semantic Networks Semantic Parsing Semantic Role Labeling Semantic Segmentation Semantic Web Semi-Supervised Learning Sensors SentencePiece Sentiment Analysis Sequence Labeling Serverless Computing Serverless GPUs Set Theory SHAP Values Siamese Networks SIFT Silhouette Score Simulated Annealing Simulation Hypothesis Simulation-to-Real Transfer (Sim2Real)Simultaneous Localization and Mapping (SLAM)SMOTE Social Acceptance of AI Social Simulation SOTA (State of the Art)spaCy Spark Speaker Diarization Spectrogram Analysis Speech Enhancement Speech Recognition Speech Synthesis Spiking Neural Networks SQL Stable Diffusion Stacking State-Action Pairs Statistical Analysis Statistical Distributions Statistics Stemming Stochastic Gradient Descent Stochastic Modeling Stochastic Processes Stop Words Stream Processing Strong AI Strong vs. Weak AI Stuart Russell Style Transfer Subword Tokenization Support Vector Machines SURF Surveillance Swarm Intelligence Symbolic AI Synthetic Data Generation Synthetic Media System Dynamics System Prompt Softmax Sampling Sequence Modeling Supervised Learning Self-Supervised Learning

T

t-SNE Teacher Forcing Technological Singularity Teleoperation Temperature Temporal Difference Learning Tensor Processing Units (TPUs)TensorFlow Testing and Validation Text Summarization Text-to-Audio Generation Text-to-Image Generation Text-to-Speech (TTS)Text-to-Video Generation TF-IDF Theano Time Series Analysis Timnit Gebru TinyML Token Limit Tokenization Tokens Tool Use (LLMs)Topic Modeling Topology Transformer Models Transformer Networks Transparency Transparency Requirements Trust Region Policy Optimization Trustworthy AI Truthfulness (in LLMs)Turing Test Tokenizer Transformer Training Data Tuning / Hyperparameter Tuning Transfer Learning

U

UMAP Unmanned Aerial Vehicles (UAVs)Unmanned Ground Vehicles U-Net Uncertainty Estimation Underfitting Unsupervised Learning Universal Approximation Theorem

V

Validation Curve Value Function Vector Database Version Control for Models Vibe code an AI Tool Video Generation Models Virtual Reality Simulations Voice Biometrics Voice Cloning Voice Conversion Vision Transformer (ViT)Vector Embedding Vanishing / Exploding Gradient Variational Autoencoder (VAE)Validation Set

W

Warmup Steps Weak AI Word Embeddings Word Sense Disambiguation WordPiece World Models 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)Yann LeCun Yoshua Bengio Yoga of AI Y-transform / YUV Y-axis / feature axis Yield (model yield / throughput)

Z

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

什麼是 K-Shot Learning

AI Fundamentals

[wˌʌt ɪz kˈeɪʃˈɑːt lˈɜːnɪŋ]

最後更新: October 15, 2025

K-Shot Learning 是一種機器學習方法，旨在少樣本學習任務中應用。它允許模型使用極少的樣本進行訓練，從而能夠在新任務上有效地執行。K-Shot 中的 'K' 指的是每個類別的樣本數量，可以是 1（One-Shot Learning）、2 或 3 等。

在傳統的機器學習中，模型訓練通常需要大量的標註數據。而 K-Shot Learning 則通過有效利用現有數據來減少對大規模數據集的依賴。這種技術在圖像識別和自然語言處理等領域得到了廣泛應用，尤其是在數據獲取成本高或挑戰性大的場景中。

K-Shot Learning 的運作方式通常包括兩個關鍵步驟：首先，通過元學習訓練模型，使其在多種任務上表現良好；其次，模型能夠利用少量樣本快速適應新任務。未來，K-Shot Learning 有望與其他深度學習技術結合，用於更複雜的任務。

K-Shot Learning 的一個優點是允許在數據稀缺的情況下進行有效學習，適用於多種實際應用。然而，它對樣本的選擇敏感，並且在樣本數量極少的情況下，仍可能導致過擬合。

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