Empirical Advocacy of Bio-inspired Models for Robust Image Recognition

• URL: http://arxiv.org/abs/2205.09037v1
• Date: Wed, 18 May 2022 16:19:26 GMT
• Title: Empirical Advocacy of Bio-inspired Models for Robust Image Recognition
• Authors: Harshitha Machiraju, Oh-Hyeon Choung, Michael H. Herzog, and Pascal Frossard
• Abstract summary: We provide a detailed analysis of such bio-inspired models and their properties. We find that bio-inspired models tend to be adversarially robust without requiring any special data augmentation. We also find that bio-inspired models tend to use both low and mid-frequency information, in contrast to other DCNN models.
• Score: 39.37304194475199
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack [60.40356882897116]
  Deep neural networks (DNNs) have shown superior performance compared to traditional image denoising algorithms. In this paper, we propose an adversarial attack method named denoising-PGD which can successfully attack all the current deep denoising models.
  arXiv  Detail & Related papers  (2023-06-28T09:30:59Z)
• Perceptual-Score: A Psychophysical Measure for Assessing the Biological Plausibility of Visual Recognition Models [9.902669518047714]
  This article proposes a new metric, Perceptual-Score, which is grounded in visual psychophysics. We perform the procedure on twelve models that vary in degree of biological inspiration and complexity. Each model's Perceptual-Score is compared against the state-of-the-art neural activity-based metric, Brain-Score.
  arXiv  Detail & Related papers  (2022-10-16T20:34:26Z)
• Large-scale Robustness Analysis of Video Action Recognition Models [10.017292176162302]
  We study robustness of six state-of-the-art action recognition models against 90 different perturbations. The study reveals some interesting findings, 1) transformer based models are consistently more robust compared to CNN based models, 2) Pretraining improves robustness for Transformer based models more than CNN based models, and 3) All of the studied models are robust to temporal perturbations for all datasets but SSv2.
  arXiv  Detail & Related papers  (2022-07-04T13:29:34Z)
• Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises [7.689542442882423]
  We designed a dual-stream vision model inspired by the human brain. This model features retina-like input layers and includes two streams: one determining the next point of focus (the fixation), while the other interprets the visuals surrounding the fixation. We evaluated this model against various benchmarks in terms of object recognition, gaze behavior and adversarial robustness.
  arXiv  Detail & Related papers  (2022-06-15T03:44:42Z)
• Are Convolutional Neural Networks or Transformers more like human vision? [9.83454308668432]
  We show that attention-based networks can achieve higher accuracy than CNNs on vision tasks. These results have implications both for building more human-like vision models, as well as for understanding visual object recognition in humans.
  arXiv  Detail & Related papers  (2021-05-15T10:33:35Z)
• On the benefits of robust models in modulation recognition [53.391095789289736]
  Deep Neural Networks (DNNs) using convolutional layers are state-of-the-art in many tasks in communications. In other domains, like image classification, DNNs have been shown to be vulnerable to adversarial perturbations. We propose a novel framework to test the robustness of current state-of-the-art models.
  arXiv  Detail & Related papers  (2021-03-27T19:58:06Z)
• Bio-inspired Robustness: A Review [46.817006169430265]
  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.
  arXiv  Detail & Related papers  (2021-03-16T18:20:29Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• Neural Additive Models: Interpretable Machine Learning with Neural Nets [77.66871378302774]
  Deep neural networks (DNNs) are powerful black-box predictors that have achieved impressive performance on a wide variety of tasks. We propose Neural Additive Models (NAMs) which combine some of the expressivity of DNNs with the inherent intelligibility of generalized additive models. NAMs learn a linear combination of neural networks that each attend to a single input feature.
  arXiv  Detail & Related papers  (2020-04-29T01:28:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.