  Nonlinear regression has been extensively employed in many computer vision
problems (e.g., crowd counting, age estimation, affective computing). Under the
umbrella of deep learning, two common solutions exist i) transforming nonlinear
regression to a robust loss function which is jointly optimizable with the deep
convolutional network, and ii) utilizing ensemble of deep networks. Although
some improved performance is achieved, the former may be lacking due to the
intrinsic limitation of choosing a single hypothesis and the latter usually
suffers from much larger computational complexity. To cope with those issues,
we propose to regress via an efficient "divide and conquer" manner. The core of
our approach is the generalization of negative correlation learning that has
been shown, both theoretically and empirically, to work well for non-deep
regression problems. Without extra parameters, the proposed method controls the
bias-variance-covariance trade-off systematically and usually yields a deep
regression ensemble where each base model is both "accurate" and "diversified".
Moreover, we show that each sub-problem in the proposed method has less
Rademacher Complexity and thus is easier to optimize. Extensive experiments on
several diverse and challenging tasks including crowd counting, personality
analysis, age estimation, and image super-resolution demonstrate the
superiority over challenging baselines as well as the versatility of the
proposed method.
