  To mitigate the burden of data labeling, we aim at improving data efficiency
for both classification and regression setups in deep learning. However, the
current focus is on classification problems while rare attention has been paid
to deep regression, which usually requires more human effort to labeling.
Further, due to the intrinsic difference between categorical and continuous
label space, the common intuitions for classification, e.g., cluster
assumptions or pseudo labeling strategies, cannot be naturally adapted into
deep regression. To this end, we first delved into the existing data-efficient
methods in deep learning and found that they either encourage invariance to
data stochasticity (e.g., consistency regularization under different
augmentations) or model stochasticity (e.g., difference penalty for predictions
of models with different dropout). To take the power of both worlds, we propose
a novel X-model by simultaneously encouraging the invariance to {data
stochasticity} and {model stochasticity}. Further, the X-model plays a minimax
game between the feature extractor and task-specific heads to further enhance
the invariance to model stochasticity. Extensive experiments verify the
superiority of the X-model among various tasks, from a single-value prediction
task of age estimation to a dense-value prediction task of keypoint
localization, a 2D synthetic, and a 3D realistic dataset, as well as a
multi-category object recognition task.
