[R] DCTD: Deep Conditional Target Densities for Accurate Regression
We propose Deep Conditional Target Densities (DCTD), a novel and general regression method with a clear probabilistic interpretation. DCTD models the conditional target density p(y|x) by using a neural network to directly predict the un-normalized density from the input-target pair (x, y). This model of p(y|x) is trained by minimizing the associated negative log-likelihood, approximated using Monte Carlo sampling. Notably, our method achieves a 1.9% AP improvement over Faster-RCNN for object detection on COCO, and sets a new state-of-the-art on visual tracking when applied for bounding box regression.
Project page: http://www.fregu856.com/publication/dctd/