Apr 7, 2020 · To improve the data quality, practitioners often need to collect multiple annotations per example and aggregate them before training models.
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Sep 25, 2019 · Review: This paper proposes a method for dealing with noisy human annotated in training data. The idea is to unify the annotation aggregation ...
Our method merges the two steps of: (i) aggregating subjective, weak, or noisy annotations, and (ii) training machine learning models. At training time, along ...
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An end-to-end framework is proposed that enables deep learning systems to learn to predict ground truth estimates directly from the available data, ...
[PDF] Prototype-Anchored Learning for Learning with Imperfect Annotations
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Extensive experiments are provided to demonstrate the superiority of PAL in handling imperfect annotations. Our main contributions are highlighted as follows: • ...
Apr 7, 2020 · Learning from Imperfect Annotations. Table 1: Accuracy across varying levels of redundancy, for all datasets we used in our experiments. For ...
We propose a simple yet effective method, namely prototype-anchored learning (PAL), which can be easily incorporated into various learning-based classification ...
L^2ID refers to a variety of studies that attempt to address challenging pattern recognition tasks by learning from limited, weak, or noisy supervision.
In this work, we investigate the impact of noisy annotations on the training and performance of a state-of-the-art CNN model for the combined task of detecting ...
Specifically, it partitions the training data into several folds and train independent NER models to identify potential mistakes in each fold. Then it adjusts ...