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什么是 K-Shot Learning - AI Glossary

首页 AI 词汇表 AI Fundamentals什么是 K-Shot Learning

AI 词汇表

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

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

B

BERT Boosting Backpropagation Batch Normalization Bias 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)

C

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

D

Discriminative Model Deterministic Model Deep Learning Data Augmentation Deepfake 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

E

Epoch Encoder Ensemble Learning Explainable AI (XAI)Embedding 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

F

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

G

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

H

Hierarchical Model Hyperparameter Hallucination Heuristic Hidden Layer 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

I

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

J

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

K

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

L

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

M

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

N

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

O

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

P

Prompt Parameter Pretraining Policy / Reinforcement Learning Policy Pooling 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

Q

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

R

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

S

Softmax Sampling Supervised Learning Sequence Modeling Self-Supervised Learning 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

T

Tokenizer Transformer Tuning / Hyperparameter Tuning Transfer Learning Training Data 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

U

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

V

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

W

Weight Decay Word Embedding Workflow Whitening / Whitening Transformation Weak Supervision Warmup Steps Weak AI Word Embeddings Word Sense Disambiguation WordPiece World Models

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)Yann LeCun Yoshua Bengio

Z

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

什么是 K-Shot Learning

AI Fundamentals

[wˌʌt ɪz kˈeɪʃˈɑːt lˈɜːnɪŋ]

最后更新: 2025年10月15日

K-Shot Learning 是一种机器学习方法，旨在少样本学习任务中应用。它允许模型使用极少的样本进行训练，从而能够在新任务上有效地执行。K-Shot 中的 'K' 指的是每个类别的样本数量，可以是 1（One-Shot Learning）、2 或 3 等。

在传统的机器学习中，模型训练通常需要大量的标注数据。而 K-Shot Learning 则通过有效利用现有数据来减少对大规模数据集的依赖。这种技术在图像识别和自然语言处理等领域得到了广泛应用，尤其是在数据获取成本高或挑战性大的场景中。

K-Shot Learning 的运作方式通常包括两个关键步骤：首先，通过元学习训练模型，使其在多种任务上表现良好；其次，模型能够利用少量样本快速适应新任务。未来，K-Shot Learning 有望与其他深度学习技术结合，用于更复杂的任务。

K-Shot Learning 的一个优点是允许在数据稀缺的情况下进行有效学习，适用于多种实际应用。然而，它对样本的选择敏感，并且在样本数量极少的情况下，仍可能导致过拟合。

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