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[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.

arXiv: https://arxiv.org/abs/1909.12297

Project page: http://www.fregu856.com/publication/dctd/

submitted by /u/dirac-hatt
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.