How well do models of visual cortex generalize to out of distribution samples?
Fig 5
Robustness to adversarial perturbation improves neural predictivity.
A: Scatter plot of neural predictivity on natural (top) and synthetic (bottom) domains as a function of robust accuracy (i.e. average of accuracy under several adversarial perturbations); B: Comparison of neural prediction score on naturalistic (top) and synthetic (bottom) stimuli across a range of models optimized for improved robustness. All error bars denote the variance across 5 repetitions of each analysis; C: Effect of train-time perturbation magnitude on neural predictivity. Here, we only included the robust models with L2 norm and similar attack settings and excluded 5 models that use different attack norm, number of steps, and step sizes; D: Scatter plot of neural predictivity scores for ResNet50 and ResNet50 () models. Each dot corresponds to an individual neuronal site.