Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4

William Berrios, Arturo Deza

Keywords: Vision Transformer, Brain-Score competition, adversarial training, rotation invariance.

Abstract: Modern high-scoring models of vision in the brain score competition do not stem from Vision Transformers. However, in this paper, we provide evidence against the unexpected trend of Vision Transformers (ViT) being not perceptually aligned with human visual representations by showing how a dual-stream Transformer, a CrossViT $~\textit{a la}$ Chen et. al. (2021), under a joint rotationally-invariant and adversarial optimization procedure yields 2nd place in the aggregate Brain-Score 2022 competition (Schrimpf et al., 2020b) averaged across all visual categories, and at the time of the competition held 1st place for the highest explainable variance of area V4. In addition, our current Transformer-based model also achieves greater explainable variance for areas V4, IT, and Behaviour than a biologically-inspired CNN (ResNet50) that integrates a frontal V1-like computation module (Dapello et al., 2020). To assess the contribution of the optimization scheme with respect to the CrossViT architecture, we perform several additional experiments on differently optimized CrossViT's regarding adversarial robustness, common corruption benchmarks, mid-ventral stimuli interpretation, and feature inversion. Against our initial expectations, our family of results provides tentative support for an $\textit{``All roads lead to Rome''}$ argument enforced via a joint optimization rule even for non biologically-motivated models of vision such as Vision Transformers.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: Neuroscience and Cognitive Science (e.g., neural coding, brain-computer interfaces)

TL;DR: We provide evidence that a specific Vision Transformer under a joint rotationally-invariant and adversarial optimization procedure can reach state of the art Brain-Score for Area V4

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2203.06649/code)