  Label distribution learning (LDL) differs from multi-label learning which
aims at representing the polysemy of instances by transforming single-label
values into descriptive degrees. Unfortunately, the feature space of the label
distribution dataset is affected by human factors and the inductive bias of the
feature extractor causing uncertainty in the feature space. Especially, for
datasets with small-scale feature spaces (the feature space dimension $\approx$
the label space), the existing LDL algorithms do not perform well. To address
this issue, we seek to model the uncertainty augmentation of the feature space
to alleviate the problem in LDL tasks. Specifically, we start with augmenting
each feature value in the feature vector of a sample into a vector (sampling on
a Gaussian distribution function). Which, the variance parameter of the
Gaussian distribution function is learned by using a sub-network, and the mean
parameter is filled by this feature value. Then, each feature vector is
augmented to a matrix which is fed into a mixer with local attention
(\textit{TabMixer}) to extract the latent feature. Finally, the latent feature
is squeezed to yield an accurate label distribution via a squeezed network.
Extensive experiments verify that our proposed algorithm can be competitive
compared to other LDL algorithms on several benchmarks.
