Papers with Code 아카이브

Active Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 1
보존된 평가 기록: 7

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/drwuHUST/IRD

공식 코드

Unsupervised Pool-Based Active Learning for Linear Regression

https://github.com/drwuHUST/iGS

공식 코드

Active Learning for Regression Using Greedy Sampling

https://github.com/drwuHUST/sAL

공식 코드

Pool-Based Sequential Active Learning for Regression

https://github.com/dsba-lab/openal

공식 코드

Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model

https://github.com/dsgissin/DiscriminativeActiveLearning

공식 코드

Discriminative Active Learning

https://github.com/dskoda/quests

공식 코드

Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory

https://github.com/dssresearch/GLISTER

공식 코드

GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning

https://github.com/duke-vision/optical-flow-active-learning-release

공식 코드

Optical Flow Training under Limited Label Budget via Active Learning

https://github.com/duojun-huang/diana-cvpr2023

공식 코드

Divide and Adapt: Active Domain Adaptation via Customized Learning

https://github.com/dwang181/active-mixup

공식 코드

Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model

https://github.com/eaplatanios/makina

공식 코드

Active Learning amidst Logical Knowledge

https://github.com/efficienttraining/labelbench

공식 코드

LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning

https://github.com/ej0cl6/deep-active-learning

공식 코드

DeepAL: Deep Active Learning in Python

https://github.com/emerging-welfare/glocongold

공식 코드

Cross-context News Corpus for Protest Events related Knowledge Base Construction

https://github.com/eugene-yang/tar-content-moderation

공식 코드

TAR on Social Media: A Framework for Online Content Moderation

https://github.com/explainableml/probvlm

공식 코드

ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models

https://github.com/eyalbd2/scratchda

공식 코드

Domain Adaptation from Scratch

https://github.com/facebookresearch/adaptiveweightsampling

공식 코드

On the Convergence of Loss and Uncertainty-based Active Learning Algorithms

https://github.com/facebookresearch/multi_view_active_learning

공식 코드

Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training

https://github.com/fastread/src

공식 코드

Finding Better Active Learners for Faster Literature Reviews

https://github.com/fbickfordsmith/epig

공식 코드

Prediction-Oriented Bayesian Active Learning

https://github.com/feedzai/research-aml-elliptic

공식 코드

Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity

https://github.com/fiveai/hgp_experiments

공식 코드

An Active Learning Reliability Method for Systems with Partially Defined Performance Functions

https://github.com/fivosts/BenchPress

공식 코드

BenchPress: A Deep Active Benchmark Generator

https://github.com/fjelenic/al-transfer

공식 코드

On Dataset Transferability in Active Learning for Transformers

https://github.com/fjxmlzn/raregan

공식 코드

RareGAN: Generating Samples for Rare Classes

https://github.com/flyingfeather/dea-sql

공식 코드

Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm

https://github.com/foadsohrabi/dl-activesensing

공식 코드

Active Sensing for Communications by Learning

https://github.com/for-ai/iterative-data-selection

공식 코드

Diversify and Conquer: Diversity-Centric Data Selection with Iterative Refinement

https://github.com/forest-snow/alps

공식 코드

Cold-start Active Learning through Self-supervised Language Modeling

https://github.com/forest-snow/clime-ui

공식 코드

Interactive Refinement of Cross-Lingual Word Embeddings

https://github.com/forest-snow/incremental-coref

공식 코드

Adapting Coreference Resolution Models through Active Learning

https://github.com/fpv-iplab/MECCANO

공식 코드

The MECCANO Dataset: Understanding Human-Object Interactions from Egocentric Videos in an Industrial-like Domain

https://github.com/francescacairoli/stoch_nsc

공식 코드

Neural Predictive Monitoring under Partial Observability

https://github.com/francescotonini/al-gtd

공식 코드

AL-GTD: Deep Active Learning for Gaze Target Detection

https://github.com/franklinnwren/ds-age

공식 코드

Dissimilar Nodes Improve Graph Active Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.