Abstract
The concept of image similarity is ambiguous, and images can be similar in one context and not in another. This ambiguity motivates the creation of metrics for specific contexts. This work explores the ability of deep perceptual similarity (DPS) metrics to adapt to a given context. DPS metrics use the deep features of neural networks for comparing images. These metrics have been successful on datasets that leverage the average human perception in limited settings. But the question remains if they could be adapted to specific similarity contexts. No single metric can suit all similarity contexts, and previous rule-based metrics are labor-intensive to rewrite for new contexts. On the other hand, DPS metrics use neural networks that might be retrained for each context. However, retraining networks takes resources and might ruin performance on previous tasks. This work examines the adaptability of DPS metrics by training ImageNet pretrained CNNs to measure similarity according to given con