CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning

Can deep learning models achieve greater generalization if their training is guided by reference to human perceptual abilities? And how can we implement this in a practical manner? This paper proposes a training strategy to ConveY Brain Oversight to Raise Generalization (CYBORG). This new approach incorporates human-annotated saliency maps into a CYBORG loss function that guides the model's learning towards features from image regions that humans find salient for the task. The Class Activation Mapping (CAM) mechanism is used to probe the model's current saliency in each training batch, juxtapose this model saliency with human saliency, and penalize large differences. Results on the task of synthetic face detection, selected to illustrate the effectiveness of the approach, show that CYBORG leads to significant improvement in accuracy on unseen samples consisting of face images generated from six Generative Adversarial Networks across multiple classification network architectures. We also show that scaling to even seven times the training data with standard loss cannot beat CYBORG accuracy. As a side effect, we observe that the addition of explicit region annotation to the task of synthetic face detection increased human classification performance. This work opens a new area of research on how to incorporate human visual saliency into loss functions in practice. All data, code and pre-trained models used in this work are offered with this paper.