# Active Learning

454 papers with code • 1 benchmarks • 14 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

## Libraries

Use these libraries to find Active Learning models and implementations## Datasets

## 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.

# 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.

# Learning Loss for Active Learning

In this paper, we propose a novel active learning method that is simple but task-agnostic, and works efficiently with the deep networks.

# Active Learning for Convolutional Neural Networks: A Core-Set Approach

active learning).

# 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.

# 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.