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Current wisdom suggests more labeled image data is always better, andobtaining labels is the bottleneck. Yet curating a pool of sufficiently diverseand informative images is itself a challenge. In particular, training imagecuration is problematic for fine-grained attributes, where the subtle visualdifferences of interest may be rare within traditional image sources. Wepropose an active image generation approach to address this issue. The mainidea is to jointly learn the attribute ranking task while also learning togenerate novel realistic image samples that will benefit that task. Weintroduce an end-to-end framework that dynamically "imagines" image pairs thatwould confuse the current model, presents them to human annotators forlabeling, then improves the predictive model with the new examples. Withresults on two datasets, we show that by thinking outside the pool of realimages, our approach gains generalization accuracy for challenging fine-grainedattribute comparisons.

Thinking Outside the Pool: Active Training Image Creation for Relative Attributes