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홈AI 용어집AI Fundamentals제로샷 학습이란 무엇인가

AI 용어집

0-9
1-shot learning2-stage detector3D convolution3D Reconstruction4D data5G + AI6DoF pose estimation7D representation8-bit quantization9-layer network0-shot learning
A
AlgorithmAutoencoderArtificial Intelligence (AI)AttentionA/B TestingAccountabilityAccuracyAcoustic ModelingActivation FunctionsActive LearningActor-Critic MethodsActuatorsAdaDeltaAdaGradAdam OptimizerAdjusted R-SquaredAdversarial AttacksAffordance LearningAgent-Based ModelingAgentic AI / Autonomous AgentsAgentic AI FrameworksAgglomerative ClusteringAGI / Artificial General IntelligenceAI AcceleratorsAI Act (EU)AI AgentsAI AlignmentAI and BiasAI and SustainabilityAI APIsAI Art GenerationAI AssistantsAI AuditAI AuditingAI Bill of Rights (US Blueprint)AI ContainmentAI DemocratizationAI Ethics BoardsAI Ethics GuidelinesAI Feature StoreAI for Climate ChangeAI Generated ContentAI Governance FrameworksAI GuardrailsAI HallucinationsAI in Healthcare EthicsAI in WarfareAI LegislationAI LiteracyAI MarketplacesAI Model GovernanceAI Model HubAI Model RegistryAI Model WeightsAI Music GenerationAI OrchestrationAI PolicyAI RegulationsAI SafetyAI SecurityAI SingularityAI Transparency ReportAI WatermarkingAI WinterAI Workflow AutomationAI-as-a-ServiceAlan TuringAlgorithmic AccountabilityAlgorithmic Bias MitigationAlgorithmic DiscriminationAlgorithmic TransparencyAndrew NgAnomaly DetectionAnomaly Detection in SecurityAnthropicApache KafkaAPI DevelopmentAPI EndpointsApriori AlgorithmArtificial General Intelligence (AGI)Artificial Neural NetworksArtificial SuperintelligenceASICsAssociation Rule LearningAsynchronous Advantage Actor-CriticAttention MechanismsAUCAudio ClassificationAudio Signal ProcessingAugmented RealityAuthenticationAuthorizationAutoencodersAutomated ReasoningAutomatic Speech Recognition (ASR)AutomationAutoMLAutonomous NavigationAutoregressive Models
B
Batch NormalizationBoostingBackpropagationBiasBag-of-Words ModelBaggingBatch SizeBayesian InferenceBayesian NetworksBayesian OptimizationBERTBias in AIBias-Variance TradeoffBig DataBig Data TechnologiesBiometric SecurityBLEU ScoreBlockchain in AIBox PlotByte-Pair Encoding (BPE)
C
Classifier / ClassificationChatbotCross-ValidationClusteringCaffeCalculusCalibrationCalifornia Consumer Privacy Act (CCPA)Canary DeploymentCapsule NetworksCarbon Footprint of AICase-Based ReasoningCatastrophic ForgettingCentral Limit TheoremChain-of-ThoughtChinese Room ArgumentClass ImbalanceClassificationCloud AI PlatformsCloud ComputingClustering AlgorithmsCNN / Convolutional Neural NetworkCode Generation ModelsCognitive ArchitecturesCognitive ComputingCohereColab NotebooksCollaborative FilteringColor SpacesComplex AnalysisComplianceCompliance Standards (ISO IEEE)Computational ComplexityComputational Fluid DynamicsComputational Theory of MindCompute-Optimal ModelsConcept DriftConceptual GraphsConditional ProbabilityConfusion MatrixConsciousness in AIConsistency ModelsConstitutional AIConstraint Satisfaction ProblemsContainerizationContent-Based FilteringContext WindowContinual LearningContinuous Integration/Continuous Deployment (CI/CD)Control SystemsConversational AIConvolutional Neural NetworksCOPPACoreference ResolutionCorrelationCorrelation MatrixCost-Sensitive LearningCross-Entropy LossCurriculum LearningCyber Threat IntelligenceCybersecurity Regulations
D
Deterministic ModelData AugmentationDeep LearningDiscriminative ModelDALL·EData AnnotationData CatalogData CentersData CleaningData DriftData GovernanceData IngestionData IntegrationData LabelingData LakeData LakesData LeakageData LineageData MiningData PipelineData PoisoningData PreprocessingData PrivacyData ProtectionData Protection LawsData QualityData SecurityData SovereigntyData TransformationData VersioningData VisualizationData Visualization TechniquesData WarehousingDatabases for AIDavies-Bouldin IndexDBSCANDecision Boundary VisualizationDecision TreesDeep Belief NetworksDeep Q-NetworksDeep Reinforcement LearningDeepfakeDeepfakesDeepMindDemis HassabisDependency ParsingDepth EstimationDescriptive StatisticsDialogue SystemsDifferential EquationsDifferential EvolutionDifferential PrivacyDiffusion ModelsDigital DivideDigital ProvenanceDigital TwinsDimensionality ReductionDirect Preference Optimization (DPO)Discourse AnalysisDiscrete Event SimulationDiscrete MathematicsDisinformationDistributed ComputingDistributed File SystemsDistributed TrainingDockerDronesDropoutDropout RegularizationDynamical Systems
E
Explainable AI (XAI)Ensemble LearningEncoderEmbeddingEarly StoppingEdge AIEdge ComputingEdge DetectionEigenvalues and EigenvectorsElon MuskEmbedding SizeEmbeddingsEmbodied AIEmergent AbilitiesEmotion RecognitionEnsemble MethodsEpisodic MemoryEpochEthical AIEthical AI GuidelinesEthical AuditingEthical Decision-MakingEthical DilemmasEthical FrameworksEthics of AIETL ProcessesEvolutionary AlgorithmsExistential RiskExpectation-MaximizationExpectation-Maximization AlgorithmExpected Calibration ErrorExpert SystemsExplainabilityExploration vs. ExploitationExploratory Data AnalysisExport Controls
F
Foundation ModelFine-tuningForward PropagationFeature ExtractionFusion / Multimodal FusionF1 ScoreFacial RecognitionFairnessFastAIFeature EngineeringFeature ImportanceFeature SelectionFeature StoreFeature StoresFederated LearningFei-Fei LiFew-Shot LearningFinite Element AnalysisFirst-Order LogicFlow MatchingForce ControlFoundation Model EconomyFoundation ModelsFourier TransformFPGAsFrame LanguagesFunctional Analysis
G
Gradient DescentGraph Neural Network (GNN)Generative AIGame Playing AIGame TheoryGame Theory SimulationsGAN / Generative Adversarial NetworkGated Recurrent UnitsGaussian Mixture ModelsGeneral Data Protection Regulation (GDPR)Generative Adversarial NetworksGenerative ModelsGenetic AlgorithmsGensimGeoffrey HintonGlobal CooperationGPT ModelsGrad-CAMGradient Boosting MachinesGradient ClippingGraph Neural NetworksGraph TheoryGraphics Processing Units (GPUs)Grid SearchGrounding
H
Hierarchical ModelHidden LayerHyperparameterHallucinationHeuristicHadoopHeatmapHelpHeuristic AlgorithmsHidden Markov ModelsHierarchical Reinforcement LearningHigh-Performance ComputingHIPAAHistogramHOGHPC ClustersHugging FaceHugging Face TransformersHuman RightsHuman-in-the-LoopHuman-Robot InteractionHyperparameter OptimizationHyperparameter Tuning
I
Imbalanced DataInstance / SampleIntelligence Amplification / AugmentationInterpretabilityIlya SutskeverImage CaptioningImage ClassificationImage RecognitionImage SegmentationImpact on EmploymentIn-Context LearningIndustrial RobotsInferenceInference EnginesInference OptimizationInferential StatisticsInformation TheoryInformed ConsentInfrastructure as CodeInstance SegmentationInstruction tuningIntellectual Property RightsIntelligent AgentsIntrusion Detection SystemsInverse Reinforcement Learning
J
JuxtapositionJoint EmbeddingJitteringJAXJohn McCarthyJoint Probability DistributionJSONL / JSON-linesJuergen SchmidhuberJupyter Notebooks
K
Knowledge DistillationKernel TrickK-means ClusteringK-Nearest NeighborsK-Shot LearningKai-Fu LeeKalman FiltersKerasKL Divergence (Kullback–Leibler Divergence)Knowledge CutoffKnowledge GraphsKnowledge RepresentationKubernetes
L
Large Language Model (LLM)Loss FunctionLatent VariableLearning RateL1 RegularizationL2 RegularizationLabel SmoothingLanguage ModelingLanguage ModelsLaplace TransformLarge Language Models (LLMs)Large Multimodal ModelsLatent Dirichlet AllocationLatent SpaceLaw of Large NumbersLayer NormalizationLearning CurveLearning Rate DecayLearning Rate SchedulingLemmatizationLIMELinear AlgebraLinear RegressionLog LossLogic ProgrammingLogistic RegressionLong Short-Term Memory NetworksLong-Context ModelsLoRA (Low-Rank Adaptation)LSTM / Long Short-Term Memory
M
Machine Learning (ML)Multimodal / MultimodalityMulti-head AttentionMeta-learningModelMachine ConsciousnessMachine TranslationMarkov Chain ModelsMarkov Chain Monte CarloMarkov Decision ProcessesMarkov ModelsMarvin MinskyMasked Language ModelsMaster Data ManagementMatplotlibMatrix DecompositionMCPMean Absolute ErrorMean Squared ErrorMechanistic InterpretabilityMel-Frequency Cepstral Coefficients (MFCCs)Metadata ManagementMicroservicesMidjourneyMind UploadingMini ToolMini-Batch Gradient DescentMixture of Experts (MoE)MLOpsMobile RobotsModel CardsModel CompressionModel DeploymentModel DriftModel Explainability ToolsModel MonitoringModel ServingModel StealingMomentum OptimizationMonitoring and LoggingMonte Carlo MethodsMonte Carlo SimulationsMoral MachinesMotion DetectionMotion PlanningMulti-Armed Bandit ProblemMultimodal AIMusic Information RetrievalMXNet
N
Novelty Detection / Anomaly DetectionNeural NetworkNormalizationn-GramsNaive Bayes AlgorithmNaive Bayes ClassifierNamed Entity RecognitionNatural Language Generation (NLG)Natural Language ProcessingNatural Language Processing (NLP)Natural Language UnderstandingNesterov Accelerated GradientNetwork SimulationsNeural Architecture SearchNeural NetworksNeural Processing Unit (NPU)Neuromorphic ComputingNick BostromNLP / Natural Language ProcessingNLTKNLU / Natural Language UnderstandingNoise ReductionNoSQL DatabasesNumPyNVIDIA CUDA
O
Objective FunctionOverfittingOnline LearningOptimizerObject DetectionObject TrackingOne-hot EncodingOntologiesOpenAIOpenAI GPTOptical Character RecognitionOptimization TheoryOut-of-Distribution (OOD) Data
P
ParameterPolicy / Reinforcement Learning PolicyPromptPretrainingPandasParallel ComputingParameter CountParameter-Efficient Fine-Tuning (PEFT)Part-of-Speech TaggingPartial Dependence PlotsPath PlanningPattern RecognitionPeople also viewedPerception in AIPerceptronPerplexityPeter NorvigPhilosophy of MindPhoneticsPipelinesPlanning and SchedulingPlotlyPolicy GradientsPolicy OptimizationPoolingPose EstimationPositional EncodingPragmaticsPrecisionPredictive ModelingPredictive ProbabilityPreference TuningPrincipal Component AnalysisPrivacyPrivacy-Preserving Machine LearningProbability Density FunctionsProbability TheoryProblem SolvingProcess ModelingProcess-Based SupervisionPrompt ChainingPrompt EngineeringPrompt InjectionPrompt MarketplacePrompt TemplatesPropositional LogicProximal Policy OptimizationPruningPyTorch
Q
QuantizationQueryQueue / BufferQuality EstimationQ-learningQLoRA (Quantized Low-Rank Adaptation)Quantum ComputingQuantum Machine LearningQuestion AnsweringQuestion Answering Systems
R
Reinforcement Learning (RL)Retrieval Augmented Generation (RAG)RegularizationRepresentation LearningR-SquaredRandom ForestsRandom SearchRay KurzweilReal AnalysisReasoning EnginesRecallRecommender SystemsRecurrent Neural NetworksRed TeamingRegressionRegression AnalysisRegulatory ComplianceReinforcement Learning from Human FeedbackReinforcement Learning in RoboticsReproducibilityResponsible AIRetrieval-Augmented GenerationReward FunctionRMSpropRNN / Recurrent Neural NetworkRobot KinematicsRobot VisionRobotic ManipulationRobotic Operating System (ROS)Robotics TransformersRobustness in AI ModelsROC CurveRodney BrooksRoot Mean Squared ErrorRule-Based Systems
S
Supervised LearningSamplingSequence ModelingSelf-Supervised LearningSaliency MapsSARSA AlgorithmScalable OversightScaling LawsScatter PlotScikit-LearnSciPySeabornSearch AlgorithmsSecure HardwareSecure Multi-Party ComputationSecure ProtocolsSelf-AttentionSelf-Driving CarsSemantic NetworksSemantic ParsingSemantic Role LabelingSemantic SegmentationSemantic WebSemi-Supervised LearningSensorsSentencePieceSentiment AnalysisSequence LabelingServerless ComputingServerless GPUsSet TheorySHAP ValuesSiamese NetworksSIFTSilhouette ScoreSimulated AnnealingSimulation HypothesisSimulation-to-Real Transfer (Sim2Real)Simultaneous Localization and Mapping (SLAM)SMOTESocial Acceptance of AISocial SimulationSoftmaxSOTA (State of the Art)spaCySparkSpeaker DiarizationSpectrogram AnalysisSpeech EnhancementSpeech RecognitionSpeech SynthesisSpiking Neural NetworksSQLStable DiffusionStackingState-Action PairsStatistical AnalysisStatistical DistributionsStatisticsStemmingStochastic Gradient DescentStochastic ModelingStochastic ProcessesStop WordsStream ProcessingStrong AIStrong vs. Weak AIStuart RussellStyle TransferSubword TokenizationSupport Vector MachinesSURFSurveillanceSwarm IntelligenceSymbolic AISynthetic Data GenerationSynthetic MediaSystem DynamicsSystem Prompt
T
Transfer LearningTokenizerTuning / Hyperparameter TuningTransformerTraining Datat-SNETeacher ForcingTechnological SingularityTeleoperationTemperatureTemporal Difference LearningTensor Processing Units (TPUs)TensorFlowTesting and ValidationText SummarizationText-to-Audio GenerationText-to-Image GenerationText-to-Speech (TTS)Text-to-Video GenerationTF-IDFTheanoTime Series AnalysisTimnit GebruTinyMLToken LimitTokenizationTokensTool Use (LLMs)Topic ModelingTopologyTransformer ModelsTransformer NetworksTransparencyTransparency RequirementsTrust Region Policy OptimizationTrustworthy AITruthfulness (in LLMs)Turing Test
U
Uncertainty EstimationUnsupervised LearningUnderfittingUniversal Approximation TheoremU-NetUMAPUnmanned Aerial Vehicles (UAVs)Unmanned Ground Vehicles
V
Validation SetVector EmbeddingVariational Autoencoder (VAE)Vanishing / Exploding GradientValidation CurveValue FunctionVector DatabaseVersion Control for ModelsVibe code an AI ToolVideo Generation ModelsVirtual Reality SimulationsVision Transformer (ViT)Voice BiometricsVoice CloningVoice Conversion
W
Whitening / Whitening TransformationWeak SupervisionWord EmbeddingWorkflowWarmup StepsWeak AIWeight DecayWord EmbeddingsWord Sense DisambiguationWordPieceWorld Models
X
X-axis / feature axisXAI / Explainable AIXLMXLNetXOR problem
Y
Yield (model yield / throughput)Yoga of AIY-transform / YUVY-axis / feature axisYAGNI (You Aren't Gonna Need It)Yann LeCunYoshua Bengio
Z
Zero-gradient phenomenonZero-centric / Zero-bias initializationZero-shot Learning / Zero-shot inferenceZygosity in augmentationZ-score NormalizationZero Trust Architecture

제로샷 학습이란 무엇인가

AI Fundamentals
마지막 업데이트: 2025년 10월 15일

제로샷 학습(0-shot learning)은 모델이 이전에 본 적이 없는 범주에서 인스턴스를 분류하거나 인식할 수 있도록 하는 머신러닝 접근 방식입니다. 이 기술은 훈련 데이터가 부족하거나 얻기 어려운 경우에 특히 유용합니다.


이 방법의 핵심은 범주의 속성이나 특징을 의미론적 정보로 표현하는 것입니다. 이를 통해 모델은 직접적인 예가 없더라도 새로운 범주의 특성을 추론할 수 있습니다. 제로샷 학습은 자연어 처리 및 컴퓨터 비전과 같은 분야에서 막대한 잠재력을 보여주며, 예를 들어 훈련받지 않은 객체의 이미지를 분류할 수 있습니다.


미래에는 AI 기술이 계속 발전함에 따라 제로샷 학습이 자동 데이터 레이블링 및 지능형 추천 시스템과 같은 다양한 응용 프로그램에서 더 중요한 역할을 할 것으로 예상됩니다. 그러나 이 기술은 포괄적이고 정확한 지식의 필요성과 특정 상황에서 발생할 수 있는 추론 오류와 같은 도전 과제에도 직면해 있습니다.

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1-shot 학습이란

1-shot 학습의 개념, 중요성, 응용 및 제한된 데이터에 대한 미래 동향을 알아보세요.

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5G + AI란 무엇인가

5G와 AI가 어떻게 기술 혁신을 이끌고 효율성을 높이며 디지털 전환을 촉진하는지 알아보세요. 보안 문제도 함께 해결합니다.

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9층 네트워크를 탐색하세요. 이 딥 러닝 모델 아키텍처는 복잡한 특징 추출 능력을 갖추고 있으며 다양한 AI 응용 프로그램에서 성능을 향상시킵니다.

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결정론적 모델이란 무엇인가

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AI Fundamentals