  Active learning algorithms select a subset of data for annotation to maximize
the model performance on a budget. One such algorithm is Expected Gradient
Length, which as the name suggests uses the approximate gradient induced per
example in the sampling process. While Expected Gradient Length has been
successfully used for classification and regression, the formulation for
regression remains intuitively driven. Hence, our theoretical contribution
involves deriving this formulation, thereby supporting the experimental
evidence. Subsequently, we show that expected gradient length in regression is
equivalent to Bayesian uncertainty. If certain assumptions are infeasible, our
algorithmic contribution (EGL++) approximates the effect of ensembles with a
single deterministic network. Instead of computing multiple possible inferences
per input, we leverage previously annotated samples to quantify the probability
of previous labels being the true label. Such an approach allows us to extend
expected gradient length to a new task: human pose estimation. We perform
experimental validation on two human pose datasets (MPII and LSP/LSPET),
highlighting the interpretability and competitiveness of EGL++ with different
active learning algorithms for human pose estimation.
