  We propose a novel loss function that dynamically rescales the cross entropy
based on prediction difficulty regarding a sample. Deep neural network
architectures in image classification tasks struggle to disambiguate visually
similar objects. Likewise, in human pose estimation symmetric body parts often
confuse the network with assigning indiscriminative scores to them. This is due
to the output prediction, in which only the highest confidence label is
selected without taking into consideration a measure of uncertainty. In this
work, we define the prediction difficulty as a relative property coming from
the confidence score gap between positive and negative labels. More precisely,
the proposed loss function penalizes the network to avoid the score of a false
prediction being significant. To demonstrate the efficacy of our loss function,
we evaluate it on two different domains: image classification and human pose
estimation. We find improvements in both applications by achieving higher
accuracy compared to the baseline methods.
