Deep neural networks set the state-of-the-art across many tasks in computer
vision, but their generalization ability to image distortions is surprisingly
fragile. In contrast, the mammalian visual system is robust to a wide range of
perturbations. Recent work suggests that this generalization ability can be
explained by useful inductive biases encoded in the representations of visual
stimuli throughout the visual cortex. Here, we successfully leveraged these
inductive biases with a multi-task learning approach: we jointly trained a deep
network to perform image classification and to predict neural activity in
macaque primary visual cortex (V1). We measured the out-of-distribution
generalization abilities of our network by testing its robustness to image
distortions. We found that co-training on monkey V1 data leads to increased
robustness despite the absence of those distortions during training.
Additionally, we showed that our network's robustness is very close to that of
an Oracle network where parts of the architecture are directly trained on noisy
images. Our results also demonstrated that the network's representations become
more brain-like as their robustness improves. Using a novel constrained
reconstruction analysis, we investigated what makes our brain-regularized
network more robust. We found that our co-trained network is more sensitive to
content than noise when compared to a Baseline network that we trained for
image classification alone. Using DeepGaze-predicted saliency maps for ImageNet
images, we found that our monkey co-trained network tends to be more sensitive
to salient regions in a scene, reminiscent of existing theories on the role of
V1 in the detection of object borders and bottom-up saliency. Overall, our work
expands the promising research avenue of transferring inductive biases from the
brain, and provides a novel analysis of the effects of our transfer.

运用多任务学习方法使深度神经网络对图片失真有着较好的鲁棒性，研究结果表明，从灵长类的视觉皮层中提取归纳偏见，使神经网络更类似于人类视觉系统，提高了其在面对失真问题时的效率。