13 datasets found

The dataset used in the paper is a graph-based active learning problem, where the goal is to achieve a low error rate while querying as few nodes as possible.

MedCATTrainer is a web-based interface for inspecting, adding and correcting biomedical NER+L models through active learning. An additional interface allows research specific...

The dataset used in the paper is a synthetic evaluation dataset by generating a point cloud drawn from a d-dimensional multivariate normal distribution.

End-to-end speech recognition systems usually require huge amounts of labeling resource, while annotating the speech data is complicated and expensive. Active learning is the...

A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

A synthetic binary dataset of desired characteristics, comprising 3000 instances with 20 features.

Active learning aims to achieve greater accuracy with less training data by selecting the most useful data samples from which it learns.

An active learning (AL) algorithm seeks to construct an effective classifier with a minimal number of labeled examples in a boot-strapping manner.

The dataset used in the paper is a collection of images from FashionMNIST, CIFAR10, CIFAR100, and STL10 datasets.

Tabular datasets for active learning on deep neural networks

Tabular datasets for active learning on deep neural networks

The dataset used for the experiments across ten models– ranging from bag-of-words models to pre-trained transformers– and find that a model having higher AUC does not necessarily...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors tested the proposed method on three real data sets for the most relevant security...