Learning Subjective Time-Series Data via Utopia Label Distribution
  Approximation
Wenxin Xu Hexin Jiang Xuefeng Liang Ying Zhou Yin Zhao Jie Zhang
Subjective time-series regression (STR) tasks have gained increasing
attention recently. However, most existing methods overlook the label
distribution bias in STR data, which results in biased models. Emerging studies
on imbalanced regression tasks, such as age estimation and depth estimation,
hypothesize that the prior label distribution of the dataset is uniform.
However, we observe that the label distributions of training and test sets in
STR tasks are likely to be neither uniform nor identical. This distinct feature
calls for new approaches that estimate more reasonable distributions to train a
fair model. In this work, we propose Utopia Label Distribution Approximation
(ULDA) for time-series data, which makes the training label distribution closer
to real-world but unknown (utopia) label distribution. This would enhance the
model's fairness. Specifically, ULDA first convolves the training label
distribution by a Gaussian kernel. After convolution, the required sample
quantity at each regression label may change. We further devise the Time-slice
Normal Sampling (TNS) to generate new samples when the required sample quantity
is greater than the initial sample quantity, and the Convolutional Weighted
Loss (CWL) to lower the sample weight when the required sample quantity is less
than the initial quantity. These two modules not only assist the model training
on the approximated utopia label distribution, but also maintain the sample
continuity in temporal context space. To the best of our knowledge, ULDA is the
first method to address the label distribution bias in time-series data.
Extensive experiments demonstrate that ULDA lifts the state-of-the-art
performance on two STR tasks and three benchmark datasets.