Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4
This work addresses the challenge of improving brain-aligned vision models for neuroscience and AI, though it is incremental as it builds on existing Transformer architectures and optimization techniques.
The paper tackled the problem of Vision Transformers not aligning with human visual representations by showing that a dual-stream Transformer under joint rotational-invariant and adversarial optimization achieved 2nd place in the aggregate Brain-Score 2022 competition and held 1st place for explainable variance in area V4, with greater explainable variance than a biologically-inspired CNN for areas V4, IT, and Behavior.
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. Code is available at https://github.com/williamberrios/BrainScore-Transformers