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Active Learning

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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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/sasanvakili/Bayesian4Wiener

공식 코드

Optimal Bayesian Affine Estimator and Active Learning for the Wiener Model

https://github.com/satoshi-kosugi/crowd-powered

공식 코드

Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter

https://github.com/savya08/active-learning-on-heteroskedastic-distributions

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Neural Active Learning on Heteroskedastic Distributions

https://github.com/sbelharbi/deep-active-learning-for-joint-classification-and-segmentation-with-weak-annotator

공식 코드

Deep Active Learning for Joint Classification & Segmentation with Weak Annotator

https://github.com/secondmind-labs/trieste

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Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

https://github.com/sejong-rcv/2023.OO4AL

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Overcoming Overconfidence for Active Learning

https://github.com/seqam-lab/alwod

공식 코드

ALWOD: Active Learning for Weakly-Supervised Object Detection

https://github.com/sgchr273/LPLgrad

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LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training

https://github.com/sgchr273/fishermask

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FisherMask: Enhancing Neural Network Labeling Efficiency in Image Classification Using Fisher Information

https://github.com/shamir-lab/rfan-trial

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Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions

https://github.com/sharat29ag/CDAL

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Contextual Diversity for Active Learning

https://github.com/sharat29ag/contextual_class

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Reducing Annotation Effort by Identifying and Labeling Contextually Diverse Classes for Semantic Segmentation Under Domain Shift

https://github.com/shenkev/Caption-Lifetime-by-Asking-Questions

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Learning to Caption Images through a Lifetime by Asking Questions

https://github.com/shib0li/mra-fno

공식 코드

Multi-Resolution Active Learning of Fourier Neural Operators

https://github.com/shijun18/paal-medseg

공식 코드

Predictive Accuracy-Based Active Learning for Medical Image Segmentation

https://github.com/shizhediao/active-cot

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Active Prompting with Chain-of-Thought for Large Language Models

https://github.com/shizhediao/active-prompt

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Active Prompting with Chain-of-Thought for Large Language Models

https://github.com/shubhomoydas/ad_examples

공식 코드

Active Anomaly Detection via Ensembles

https://github.com/shuguoj/hsi-classification

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A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples

https://github.com/shuvenduroy/activefer

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Active Learning with Contrastive Pre-training for Facial Expression Recognition

https://github.com/siddk/vqa-outliers

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Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering

https://github.com/simula-complex/evoclinical

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EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry System

https://github.com/sinhasam/vaal

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Variational Adversarial Active Learning

https://github.com/siplab-gt/APM-LR

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Feedback Coding for Active Learning

https://github.com/siyuhuang/TOD

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Semi-Supervised Active Learning with Temporal Output Discrepancy

https://github.com/slautin/2024_co-navigation

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Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning

https://github.com/slds-lmu/wildlife-experiments

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Automated wildlife image classification: An active learning tool for ecological applications

https://github.com/slds-lmu/wildlife-ml

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Automated wildlife image classification: An active learning tool for ecological applications

https://github.com/smallcube/EAL-GAN

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Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning

https://github.com/smiletoe/hpal

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Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation

https://github.com/smuradoglu/almorphinfl

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Eeny, meeny, miny, moe. How to choose data for morphological inflection

https://github.com/snu-mllab/greedypolicyformoco

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Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization

https://github.com/socialmediaie/pytail

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PyTAIL: Interactive and Incremental Learning of NLP Models with Human in the Loop for Online Data

https://github.com/sophiaalthammer/alforrankers

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Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection

https://github.com/spectraldani/pai

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Parallel MCMC Without Embarrassing Failures

https://github.com/spirit-moon-fly/CAL

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Causal-Guided Active Learning for Debiasing Large Language Models

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