Adaptive truncated residual regression for fine-grained regression problems


Hirotaka Hachiya, Yu Yamamoto, Kazuro Hirahara, Naonori Ueda ;
Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:868-882, 2019.


Recently, anchor-based regression methods have been applied to challenging regression problems, e.g., object detection and distance estimation, and greatly improved those performances. The key idea of anchor-based regression is to solve the regression of the residuals between selected anchors and original target variable, where the variance is expected to be smaller. However, similar to an ordinary regression method, the anchor-based regression could face difficulty on a fine-grained regression and ill-posed problems where the residual variables tend to be too small and complicated to accurately predict. To overcome these problems on the anchor-based regression, we propose to introduce an adaptive residual encoding in which the too small residual is magnified, and the too-large residual is truncated using adaptively tuned sigmoidal function. Our proposed method, called ATR-Nets (Adaptive Truncated Residual-Networks) with an end-to-end architecture could control the range of the target residual to be fitted based on the regression performance, Through experiments with toy-data and the system identification for earthquake asperity models, we show the effectiveness of our proposed method.

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