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
Most implemented papers
Self-Regulated Interactive Sequence-to-Sequence Learning
Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning.
Few-Shot Learning with Graph Neural Networks
We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.
Variational Adversarial Active Learning
Unlike conventional active learning algorithms, our approach is task agnostic, i. e., it does not depend on the performance of the task for which we are trying to acquire labeled data.
Learning Loss for Active Learning
Mephisto405/Learning-Loss-for-Active-Learning
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CVPR 2019
In this paper, we propose a novel active learning method that is simple but task-agnostic, and works efficiently with the deep networks.
Deep Bayesian Active Learning with Image Data
In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way.
Active learning in annotating micro-blogs dealing with e-reputation
This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i. e., representation, web reputation) of politicians.
libact: Pool-based Active Learning in Python
libact is a Python package designed to make active learning easier for general users.
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
We design a new algorithm for batch active learning with deep neural network models.
Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks
We present a novel active learning algorithm, termed as iterative surrogate model optimization (ISMO), for robust and efficient numerical approximation of PDE constrained optimization problems.
