Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.

Awesome Active Learning Paper List

[MedIA] Paper list and source code for survey "A comprehensive survey on deep active learning in medical image analysis"

Tornado is an open source Human-in-the-loop machine learning tool. It helps you label your dataset on the fly while training your model through a simple web user interface. It supports all data types: structured, text and image.

Code accompanying EMNLP 2020 paper "Cold-start Active Learning through Self-supervised Language Modeling".

Chatbot utilizando o conceito de Active Learning com o Bot Framework v3 e o LUIS (Microsoft Cognitive Services)

The demo partially associated with the following papers: "Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images" and "Multiclass Non-Randomized Spectral–Spatial Active Learning for Hyperspectral Image Classification".

Non-Uniform Subset Selection for Active Learning in Structured Data

A collection of ML-related active learning datasets, including algorithms, .ipynb pipelines, .py scripts and curated and ethically aligned synthetic data.

KL-Ergodic Exploration from Equilibrium

The following demo comes for two papers "Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification" and "Multi-layer Extreme Learning Machine-based Autoencoder for Hyperspectral Image Classification".

Chatbot utilizando o conceito de Active Learning com o Bot Framework v4 e o LUIS (Microsoft Cognitive Services)

Code for the paper "Reinforced Active Learning for Image Segmentation", adapted to the medical domain for 3D anatomical and pathological segmentation.

Chatbot integrado ao LUIS e ao QnA Maker com Active Learning em um cenário híbrido

Official codebase for "Overcoming Overconfidence for Active Learning." IEEE Access (2024)

A Comparative Evaluation of Active Learning Methods in Deep Recommendation

Strata 2018 Tutorial: R and Python for Scalable Data Science

Official code for the IEEE RAL paper: CLEVER: Stream-Based Active Learning for Robust Semantic Perception From Human Instructions

A wrapper to use GRACE MLIP as a substitute for VASP/DFT backend to do various solid state systems advance analysis