@article {151, title = {Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations}, journal = {Neural Information Processing Systems (NeurIPS; spotlight)}, year = {2020}, month = {June 17, 2020}, type = {preprint}, abstract = {

Current state-of-the-art object recognition models are largely based on convolutional neural network (CNN) architectures, which are loosely inspired by the primate visual system. However, these CNNs can be fooled by imperceptibly small, explicitly crafted perturbations, and struggle to recognize objects in corrupted images that are easily recognized by humans. Here, by making comparisons with primate neural data, we first observed that CNN models with a neural hidden layer that better matches primate primary visual cortex (V1) are also more robust to adversarial attacks. Inspired by this observation, we developed VOneNets, a new class of hybrid CNN vision models. Each VOneNet contains a fixed weight neural network front-end that simulates primate V1, called the VOneBlock, followed by a neural network back-end adapted from current CNN vision models. The VOneBlock is based on a classical neuroscientific model of V1: the linear-nonlinear-Poisson model, consisting of a biologically-constrained Gabor filter bank, simple and complex cell nonlinearities, and a V1 neuronal stochasticity generator. After training, VOneNets retain high ImageNet performance, but each is substantially more robust, outperforming the base CNNs and state-of-the-art methods by 18\% and 3\%, respectively, on a conglomerate benchmark of perturbations comprised of white box adversarial attacks and common image corruptions. Finally, we show that all components of the VOneBlock work in synergy to improve robustness. While current CNN architectures are arguably brain-inspired, the results presented here demonstrate that more precisely mimicking just one stage of the primate visual system leads to new gains in ImageNet-level computer vision applications.

}, doi = {10.1101/2020.06.16.154542}, url = {https://www.biorxiv.org/content/10.1101/2020.06.16.154542v27}, author = {Dapello, Joel and Marques, Tiago and Schrimpf, Martin and Geiger, Franziska and Cox, David D and DiCarlo, James J} } @article {65, title = {Why is Real-World Visual Object Recognition Hard?}, journal = {PLoS Computational Biology}, volume = {4}, year = {2008}, month = {01/2008}, pages = {e27}, abstract = {

Progress in understanding the brain mechanisms underlying vision requires the construction of computational models that not only emulate the brain\&$\#$39;s anatomy and physiology, but ultimately match its performance on visual tasks. In recent years, \"natural\" images have become popular in the study of vision and have been used to show apparently impressive progress in building such models. Here, we challenge the use of uncontrolled \"natural\" images in guiding that progress. In particular, we show that a simple V1-like model\–a neuroscientist\&$\#$39;s \"null\" model, which should perform poorly at real-world visual object recognition tasks\–outperforms state-of-the-art object recognition systems (biologically inspired and otherwise) on a standard, ostensibly natural image recognition test. As a counterpoint, we designed a \"simpler\" recognition test to better span the real-world variation in object pose, position, and scale, and we show that this test correctly exposes the inadequacy of the V1-like model. Taken together, these results demonstrate that tests based on uncontrolled natural images can be seriously misleading, potentially guiding progress in the wrong direction. Instead, we reexamine what it means for images to be natural and argue for a renewed focus on the core problem of object recognition\–real-world image variation.

}, doi = {10.1371/journal.pcbi.0040027}, url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.0040027}, author = {Pinto, Nicolas and Cox, David D and DiCarlo, James J}, editor = {Friston, Karl J} } @article {90, title = {{\textquoteright}Breaking{\textquoteright} position-invariant object recognition}, journal = {Nature Neuroscience}, volume = {8}, year = {2005}, month = {08/2005}, pages = {1145 - 1147}, abstract = {

While it is often assumed that objects can be recognized irrespective of where they fall on the retina, little is known about the mechanisms underlying this ability. By exposing human subjects to an altered world where some objects systematically changed identity during the transient blindness that accompanies eye movements, we induced predictable object confusions across retinal positions, effectively \&$\#$39;breaking\&$\#$39; position invariance. Thus, position invariance is not a rigid property of vision but is constantly adapting to the statistics of the environment.

}, issn = {1097-6256}, doi = {10.1038/nn1519}, url = {http://www.nature.com/articles/nn1519.pdf}, author = {Cox, David D and Meier, Philip and Oertelt, Nadja and DiCarlo, James J} }