Official codebase for "Overcoming Overconfidence for Active Learning." IEEE Access (2024)
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Updated
Sep 18, 2024 - Python
Official codebase for "Overcoming Overconfidence for Active Learning." IEEE Access (2024)
Official implementation of our AISTATS 2023 paper "FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery".
I built a data labaling platform with active learing and human feedback in the loop.
Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.
[MedIA] Accompanying paper list and source code for survey "A comprehensive survey on deep active learning in medical image analysis"
Awesome Active Learning Paper List
python code + pytorch + active learning + LeNet - 5 model
Chatbot utilizando o conceito de Active Learning com o Bot Framework v3 e o LUIS (Microsoft Cognitive Services)
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.
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".
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".
Code for the paper "Reinforced Active Learning for Image Segmentation", adapted to the medical domain for 3D anatomical and pathological segmentation.
The code in this repository makes it possible to reproduce the results of my master thesis "Evaluation of the performance of neural network models and classical models in the context of Active Learning for Systematic Reviewing"
Code accompanying EMNLP 2020 paper "Cold-start Active Learning through Self-supervised Language Modeling".
KL-Ergodic Exploration from Equilibrium
robotics
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