1 citations · 2 across the 9 of their papers we have counts for
9 papers
BoSS: A Best-of-Strategies Selector as an Oracle for Deep Active Learning
Denis Huseljic, Paul Hahn, Marek Herde +2
Active learning (AL) aims to reduce annotation costs while maximizing model performance by iteratively selecting valuable instances. While foundation models have made it easier to…
Cleaning the Pool: Progressive Filtering of Unlabeled Pools in Deep Active Learning
Denis Huseljic, Marek Herde, Lukas Rauch +2
Existing active learning (AL) strategies capture fundamentally different notions of data value, e.g., uncertainty or representativeness. Consequently, the effectiveness of strategi…
No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success
Lukas Rauch, Moritz Wirth, Denis Huseljic +3
The advent of large language models (LLMs) capable of producing general-purpose representations lets us revisit the practicality of deep active learning (AL): By leveraging frozen…
crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels
Marek Herde, Lukas Lührs, Denis Huseljic +1
Crowdworking is a cost-efficient solution for acquiring class labels. Since these labels are subject to noise, various approaches to learning from crowds have been proposed. Typica…
dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans
Marek Herde, Denis Huseljic, Lukas Rauch +1
Human annotators typically provide annotated data for training machine learning models, such as neural networks. Yet, human annotations are subject to noise, impairing generalizati…
Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension
Marek Herde, Lukas Lührs, Denis Huseljic +1
Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by…
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification
Denis Huseljic, Paul Hahn, Marek Herde +2
Deep active learning (AL) seeks to minimize the annotation costs for training deep neural networks. BAIT, a recently proposed AL strategy based on the Fisher Information, has demon…
Active Label Refinement for Semantic Segmentation of Satellite Images
Tuan Pham Minh, Jayan Wijesingha, Daniel Kottke +5
Remote sensing through semantic segmentation of satellite images contributes to the understanding and utilisation of the earth's surface. For this purpose, semantic segmentation ne…
Efficient Bayesian Updates for Deep Active Learning via Laplace Approximations
Denis Huseljic, Marek Herde, Lukas Rauch +5
Deep active learning (AL) selects batches of instances for annotation to avoid retraining deep neural networks (DNNs) after each new label. Employing a naive top- selection can…