Sep 13, 2021 · This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness.
Sep 13, 2021 · We empirically demonstrate our proposed methods reduce sampling bias, achieve state-of- the-art accuracy and model calibration in an active.
This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness ...
Sep 13, 2021 · This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and ...
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This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness.
Jun 25, 2022 · Sampling bias is a known issue within active-learning paradigms; this occurs when an active learning process over- or undersamples specific ...
Jul 13, 2024 · In this work, we focus on investigating the performance of common active learning (AL) algorithms under spurious bias and designing an AL algorithm that is ...
This repository contains the code for our EMNLP '19 paper: Sampling Bias in Deep Active Classification: An Empirical Study
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We demonstrate our proposed method reduces sampling bias, achieves state-of-the-art accuracy and model calibration in an active learning setup with the query ...
Dec 1, 2022 · The current paper proposes two novel approaches to counteract the effects of sampling bias: semi-supervised learning, and discriminative classification models.