Active Learning
754 papers with code • 1 benchmarks • 15 datasets
Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model
Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads
Benchmarks
These leaderboards are used to track progress in Active Learning
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Libraries
Use these libraries to find Active Learning models and implementationsDatasets
CIFAR-10
MNIST-8M
DeepWeeds
HJDataset
Latest papers
CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imagery
DPIRD-DMA/CloudS2Mask
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Remote Sensing of Environment 2024
Precise and efficient cloud and cloud shadow masking methods are required for the automated use of this data.
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning
Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop automated experiment, is emerging as a crucial objective for accelerating scientific research.
Information theory unifies atomistic machine learning, uncertainty quantification, and materials thermodynamics
An accurate description of information is relevant for a range of problems in atomistic modeling, such as sampling methods, detecting rare events, analyzing datasets, or performing uncertainty quantification (UQ) in machine learning (ML)-driven simulations.
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification
BAIT, a recently proposed AL strategy based on the Fisher Information, has demonstrated impressive performance across various datasets.
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets
By dynamically selecting different anchors at each iteration it promotes class balance and prevents overfitting the initial decision boundary, thus promoting the discovery of new clusters of minority instances.
Active Test-Time Adaptation: Theoretical Analyses and An Algorithm
Extensive experimental results confirm consistency with our theoretical analyses and show that the proposed ATTA method yields substantial performance improvements over TTA methods while maintaining efficiency and shares similar effectiveness to the more demanding active domain adaptation (ADA) methods.
Conversational Disease Diagnosis via External Planner-Controlled Large Language Models
danielsun94/conversational_diagnosis
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The advancement of medical artificial intelligence (AI) has set the stage for the realization of conversational diagnosis, where AI systems mimic human doctors by engaging in dialogue with patients to deduce diagnoses.
Few-shot Named Entity Recognition via Superposition Concept Discrimination
Then a superposition instance retriever is applied to retrieve corresponding instances of these superposition concepts from large-scale text corpus.
Generative Active Learning for Image Synthesis Personalization
The primary challenge in conducting active learning on generative models lies in the open-ended nature of querying, which differs from the closed form of querying in discriminative models that typically target a single concept.
From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning
johnmartinsson/adaptive-change-point-detection
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In this work we propose an audio recording segmentation method based on an adaptive change point detection (A-CPD) for machine guided weak label annotation of audio recording segments.
