Generating Unbiased Pseudo-labels via a Theoretically Guaranteed
  Chebyshev Constraint to Unify Semi-supervised Classification and Regression
Jiaqi Wu Junbiao Pang Qingming Huang
Both semi-supervised classification and regression are practically
challenging tasks for computer vision. However, semi-supervised classification
methods are barely applied to regression tasks. Because the threshold-to-pseudo
label process (T2L) in classification uses confidence to determine the quality
of label. It is successful for classification tasks but inefficient for
regression tasks. In nature, regression also requires unbiased methods to
generate high-quality labels. On the other hand, T2L for classification often
fails if the confidence is generated by a biased method. To address this issue,
in this paper, we propose a theoretically guaranteed constraint for generating
unbiased labels based on Chebyshev's inequality, combining multiple predictions
to generate superior quality labels from several inferior ones. In terms of
high-quality labels, the unbiased method naturally avoids the drawback of T2L.
Specially, we propose an Unbiased Pseudo-labels network (UBPL network) with
multiple branches to combine multiple predictions as pseudo-labels, where a
Feature Decorrelation loss (FD loss) is proposed based on Chebyshev constraint.
In principle, our method can be used for both classification and regression and
can be easily extended to any semi-supervised framework, e.g. Mean Teacher,
FixMatch, DualPose. Our approach achieves superior performance over SOTAs on
the pose estimation datasets Mouse, FLIC and LSP, as well as the classification
datasets CIFAR10/100 and SVHN.