  Given an imperfect predictor, we exploit additional features at test time to
improve the predictions made, without retraining and without knowledge of the
prediction function. This scenario arises if training labels or data are
proprietary, restricted, or no longer available, or if training itself is
prohibitively expensive. We assume that the additional features are useful if
they exhibit strong statistical dependence to the underlying perfect predictor.
Then, we empirically estimate and strengthen the statistical dependence between
the initial noisy predictor and the additional features via manifold denoising.
As an example, we show that this approach leads to improvement in real-world
visual attribute ranking. Project webpage: http://www.jamestompkin.com/tupi
