TITLE: Bias-Aware Loss for Training Image and Speech Quality Prediction Models from Multiple Datasets 

ABSTRACT: The ground truth used for training image, video, or speech quality prediction
models is based on the Mean Opinion Scores (MOS) obtained from subjective
experiments. Usually, it is necessary to conduct multiple experiments, mostly
with different test participants, to obtain enough data to train quality models
based on machine learning. Each of these experiments is subject to an
experiment-specific bias, where the rating of the same file may be
substantially different in two experiments (e.g. depending on the overall
quality distribution). These different ratings for the same distortion levels
confuse neural networks during training and lead to lower performance. To
overcome this problem, we propose a bias-aware loss function that estimates
each dataset's biases during training with a linear function and considers it
while optimising the network weights. We prove the efficiency of the proposed
method by training and validating quality prediction models on synthetic and
subjective image and speech quality datasets.
The ground truth used for training image, video, or speech quality prediction
models is based on the Mean Opinion Scores (MOS) obtained from subjective
experiments. Usually, it is necessary to conduct multiple experiments, mostly
with different test participants, to obtain enough data to train quality models
based on machine learning. Each of these experiments is subject to an
experiment-specific bias, where the rating of the same file may be
substantially different in two experiments (e.g. depending on the overall
quality distribution). These different ratings for the same distortion levels
confuse neural networks during training and lead to lower performance. To
overcome this problem, we propose a bias-aware loss function that estimates
each dataset's biases during training with a linear function and considers it
while optimising the network weights. We prove the efficiency of the proposed
method by training and validating quality prediction models on synthetic and
subjective image and speech quality datasets.
Bias-Aware Loss for Training Image and Speech Quality Prediction Models from Multiple Datasets
http://arxiv.org/abs/2104.10217v1