Deep neural networks are used for a wide range of regression problems.
However, there exists a significant gap in accuracy between specialized
approaches and generic direct regression in which a network is trained by
minimizing the squared or absolute error of output labels. Prior work has shown
that solving a regression problem with a set of binary classifiers can improve
accuracy by utilizing well-studied binary classification algorithms. We
introduce binary-encoded labels (BEL), which generalizes the application of
binary classification to regression by providing a framework for considering
arbitrary multi-bit values when encoding target values. We identify desirable
properties of suitable encoding and decoding functions used for the conversion
between real-valued and binary-encoded labels based on theoretical and
empirical study. These properties highlight a tradeoff between classification
error probability and error-correction capabilities of label encodings. BEL can
be combined with off-the-shelf task-specific feature extractors and trained
end-to-end. We propose a series of sample encoding, decoding, and training loss
functions for BEL and demonstrate they result in lower error than direct
regression and specialized approaches while being suitable for a diverse set of
regression problems, network architectures, and evaluation metrics. BEL
achieves state-of-the-art accuracies for several regression benchmarks. Code is
available at this https URL

提出使用 binary-encoded labels 来进行回归问题，该方法可以通过利用二进制分类算法来提高准确性并具有误差校正功能，与其它专门化方法和直接回归方法相比较，有更低的误差