Parting with Illusions about Deep Active Learning

Parting with Illusions about Deep Active Learning

11 December 2019

Sudhanshu Mittal

Maxim Tatarchenko

Özgün Çiçek

Thomas Brox

Papers citing "Parting with Illusions about Deep Active Learning"

15 / 15 papers shown

Title
Efficient Data Selection for Training Genomic Perturbation Models G. Panagopoulos J. Lutzeyer Sofiane Ennadir Michalis Vazirgiannis Jun Pang 144 0 0 18 Mar 2025
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training Paul Doucet Benjamin Estermann Till Aczél Roger Wattenhofer 150 4 0 20 Jan 2025
SUPClust: Active Learning at the Boundaries Yuta Ono Till Aczél Benjamin Estermann Roger Wattenhofer 33 1 0 06 Mar 2024
Direct Acquisition Optimization for Low-Budget Active Learning Zhuokai Zhao Yibo Jiang Yuxin Chen 31 1 0 08 Feb 2024
Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning Vladan Stojnić Zakaria Laskar Giorgos Tolias 29 0 0 07 Jul 2023
IAdet: Simplest human-in-the-loop object detection Franco Marchesoni-Acland Gabriele Facciolo VLM 44 1 0 04 Jul 2023
An Empirical Study on the Efficacy of Deep Active Learning for Image Classification Yu Li Mu-Hwa Chen Yannan Liu Daojing He Qiang Xu 26 9 0 30 Nov 2022
Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets Yuan-Hong Liao Amlan Kar Sanja Fidler VLM 20 29 0 26 Apr 2021
On Initial Pools for Deep Active Learning Akshay L Chandra Sai Vikas Desai Chaitanya Devaguptapu V. Balasubramanian 19 19 0 30 Nov 2020
Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings Viraj Prabhu Arjun Chandrasekaran Kate Saenko Judy Hoffman OOD 95 124 0 16 Oct 2020
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost M. Gao Zizhao Zhang Guo-Ding Yu Sercan Ö. Arik L. Davis Tomas Pfister 158 195 0 16 Oct 2019
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average Ben Athiwaratkun Marc Finzi Pavel Izmailov A. Wilson 197 243 0 14 Jun 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 270 5,660 0 05 Dec 2016
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference Y. Gal Zoubin Ghahramani UQCV BDL 197 745 0 06 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 261 9,136 0 06 Jun 2015