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We propose a novel loss function that dynamically rescales the cross entropybased on prediction difficulty regarding a sample. Deep neural networkarchitectures in image classification tasks struggle to disambiguate visuallysimilar objects. Likewise, in human pose estimation symmetric body parts oftenconfuse the network with assigning indiscriminative scores to them. This is dueto the output prediction, in which only the highest confidence label isselected without taking into consideration a measure of uncertainty. In thiswork, we define the prediction difficulty as a relative property coming fromthe confidence score gap between positive and negative labels. More precisely,the proposed loss function penalizes the network to avoid the score of a falseprediction being significant. To demonstrate the efficacy of our loss function,we evaluate it on two different domains: image classification and human poseestimation. We find improvements in both applications by achieving higheraccuracy compared to the baseline methods.

Anchor Loss: Modulating Loss Scale based on Prediction Difficulty