  Active learning continues to remain significant in the industry since it is
data efficient. Not only is it cost effective on a constrained budget,
continuous refinement of the model allows for early detection and resolution of
failure scenarios during the model development stage. Identifying and fixing
failures with the model is crucial as industrial applications demand that the
underlying model performs accurately in all foreseeable use cases. One popular
state-of-the-art technique that specializes in continuously refining the model
via failure identification is Learning Loss. Although simple and elegant, this
approach is empirically motivated. Our paper develops a foundation for Learning
Loss which enables us to propose a novel modification we call LearningLoss++.
We show that gradients are crucial in interpreting how Learning Loss works,
with rigorous analysis and comparison of the gradients between Learning Loss
and LearningLoss++. We also propose a convolutional architecture that combines
features at different scales to predict the loss. We validate LearningLoss++
for regression on the task of human pose estimation (using MPII and LSP
datasets), as done in Learning Loss. We show that LearningLoss++ outperforms in
identifying scenarios where the model is likely to perform poorly, which on
model refinement translates into reliable performance in the open world.
