# Empirical Advocacy of Bio-inspired Models for Robust Image Recognition

arXiv: https://arxiv.org/abs/2205.09037 · HTML: https://mlsanity.com/paper/2205.09037/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 17 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 15 of 15)

- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [Adversarial Examples Are Not Bugs, They Are Features](https://mlsanity.com/paper/1905.02175/) (2019)
- [RobustBench: a standardized adversarial robustness benchmark](https://mlsanity.com/paper/2010.09670/) (2020)
- [A Fourier Perspective on Model Robustness in Computer Vision](https://mlsanity.com/paper/1906.08988/) (2019)
- [Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack](https://mlsanity.com/paper/1811.09310/) (2018)
- [Adversarial Examples that Fool both Computer Vision and Time-Limited Humans](https://mlsanity.com/paper/1802.08195/) (2018)
- [On the effectiveness of adversarial training against common corruptions](https://mlsanity.com/paper/2103.02325/) (2021)
- [Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs](https://mlsanity.com/paper/2110.02797/) (2021)
- [Adversarial Robustness May Be at Odds With Simplicity](https://mlsanity.com/paper/1901.00532/) (2019)
- [Learning From Brains How to Regularize Machines](https://mlsanity.com/paper/1911.05072/) (2019)
- [Hold me tight! Influence of discriminative features on deep network boundaries](https://mlsanity.com/paper/2002.06349/) (2020)
- [Biologically Inspired Mechanisms for Adversarial Robustness](https://mlsanity.com/paper/2006.16427/) (2020)
- [Neural Networks with Recurrent Generative Feedback](https://mlsanity.com/paper/2007.09200/) (2020)
- [Crowding Reveals Fundamental Differences in Local vs. Global Processing in Humans and Machines](https://mlsanity.com/paper/2004.12676/) (2020)

## Built on by (most cited first, 3 of 3)

- [CLAD: A Contrastive Learning based Approach for Background Debiasing](https://mlsanity.com/paper/2210.02748/) (2022)
- [Frequency-Based Vulnerability Analysis of Deep Learning Models against Image Corruptions](https://mlsanity.com/paper/2306.07178/) (2023)
- [Improving Robustness via Tilted Exponential Layer: A Communication-Theoretic Perspective](https://mlsanity.com/paper/2311.01047/) (2023)

## Abstract (arXiv)

Deep convolutional neural networks (DCNNs) have revolutionized computer vision and are often advocated as good models of the human visual system. However, there are currently many shortcomings of DCNNs, which preclude them as a model of human vision. There are continuous attempts to use features of the human visual system to improve the robustness of neural networks to data perturbations. We provide a detailed analysis of such bio-inspired models and their properties. To this end, we benchmark the robustness of several bio-inspired models against their most comparable baseline DCNN models. We find that bio-inspired models tend to be adversarially robust without requiring any special data augmentation. Additionally, we find that bio-inspired models beat adversarially trained models in the presence of more real-world common corruptions. Interestingly, we also find that bio-inspired models tend to use both low and mid-frequency information, in contrast to other DCNN models. We find that this mix of frequency information makes them robust to both adversarial perturbations and common corruptions.