JOINT ROTATIONAL INVARIANCE AND ADVERSARIAL TRAINING OF A DUAL-STREAM TRANSFORMER YIELDS STATE OF THE ART BRAIN-SCORE FOR AREA V4 Anonymous

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 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 Behavior 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 "All roads lead to Rome" argument enforced via a joint optimization rule even for non biologically-motivated models of vision such as Vision Transformers.

1. INTRODUCTION

Research and design of modern deep learning and computer vision systems such as the NeoCognitron (Fukushima & Miyake, 1982) , H-Max Model (Serre et al., 2005) and classical CNNs (LeCun et al., 2015) have often stemmed from breakthroughs in visual neuroscience dating from Kuffler (1953) and Hubel & Wiesel (1962) . Today, research in neuroscience passes through a phase of symbiotic development where several models of artificial visual computation (mainly deep neural networks), may inform visual neuroscience (Richards et al., 2019) Of particular recent interest is the development of Vision Transformers (Dosovitskiy et al., 2021) . A model that originally generated several great breakthroughs in natural language processing (Vaswani et al., 2017) , and that has now slowly begun to dominate the field of machine visual computation. However, in computer vision, we still do not understand why Vision Transformers perform so well when adapted to the visual domain (Bhojanapalli et al., 2021) . Is this improvement in performance due to their self-attention mechanism; a relaxation of their weight-sharing constraint? Their greater number of parameters? Their optimization procedure? Or perhaps a combination of all these factors? Naturally, given the uncertainty of the models' explainability, their use has been carefully limited as a model of visual computation in biological (human) vision. This is a double-edged sword: On one hand, perceptual psychologists still rely heavily on relatively low-scoring ImageNet-based accuracy models such as AlexNet, ResNet & VGG despite their limited degree of biological plausibility (though some operations are preserved, eg. local filtering, half-wave rectification, pooling). On the other hand, a new breed of models such as Vision Transformers has surged, but their somewhat non-biologically inspired computations have no straightforward mapping 1



shedding light on puzzles of development (Lindsey et al., 2019), physiology (Dapello et al., 2020), representation (Jagadeesh & Gardner, 2022) and perception (Harrington & Deza, 2022).

