Machine learningMachine learning
Self-supervised Learning
Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.
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Active Learning Federated LearningActive Learning Self-supervised LearningContrastive Learning for NLPEnsemble Self-supervised LearningEnsemble Semi-supervised LearningFew-shot LearningMetric LearningOnline LearningOnline Self-supervised LearningOnline Semi-supervised learningRegularized Few-Shot LearningRegularized semi-supervised learningSelf-supervised Active LearningSelf-supervised Autoencoder Anomaly DetectionSelf-supervised BoostingSelf-supervised DBSCANSelf-supervised Federated learningSelf-supervised Gaussian ProcessSelf-supervised Instance SegmentationSelf-supervised K-meansSelf-supervised K-nearest neighborsSelf-supervised LightGBMSelf-supervised Logistic RegressionSelf-supervised Metric learningSelf-supervised Naive BayesSelf-supervised One-class SVMSelf-supervised Random ForestSelf-supervised Support Vector MachineSelf-supervised Transfer learningSemi-supervised Federated learningSemi-supervised Few-shot LearningSemi-supervised Gradient BoostingSemi-supervised LearningSemi-supervised Metric LearningSemi-supervised Transfer LearningSemi-supervised Voting EnsembleTransfer LearningWeakly Supervised Semantic SegmentationWeakly supervised text summarizationWeakly supervised vision transformer