Retinotopic Mapping Enhances the Robustness of Convolutional Neural Networks

• URL: http://arxiv.org/abs/2402.15480v1
• Date: Fri, 23 Feb 2024 18:15:37 GMT
• Title: Retinotopic Mapping Enhances the Robustness of Convolutional Neural Networks
• Authors: Jean-Nicolas J\'er\'emie and Emmanuel Dauc\'e and Laurent U Perrinet
• Abstract summary: This study investigates whether retinotopic mapping, a critical component of foveated vision, can enhance image categorization and localization performance. Renotopic mapping was integrated into the inputs of standard off-the-shelf convolutional neural networks (CNNs) Surprisingly, the retinotopically mapped network achieved comparable performance in classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Foveated vision, a trait shared by many animals, including humans, has not been fully utilized in machine learning applications, despite its significant contributions to biological visual function. This study investigates whether retinotopic mapping, a critical component of foveated vision, can enhance image categorization and localization performance when integrated into deep convolutional neural networks (CNNs). Retinotopic mapping was integrated into the inputs of standard off-the-shelf convolutional neural networks (CNNs), which were then retrained on the ImageNet task. As expected, the logarithmic-polar mapping improved the network's ability to handle arbitrary image zooms and rotations, particularly for isolated objects. Surprisingly, the retinotopically mapped network achieved comparable performance in classification. Furthermore, the network demonstrated improved classification localization when the foveated center of the transform was shifted. This replicates a crucial ability of the human visual system that is absent in typical convolutional neural networks (CNNs). These findings suggest that retinotopic mapping may be fundamental to significant preattentive visual processes.

Related papers

• Coding schemes in neural networks learning classification tasks [52.22978725954347]
  We investigate fully-connected, wide neural networks learning classification tasks. We show that the networks acquire strong, data-dependent features. Surprisingly, the nature of the internal representations depends crucially on the neuronal nonlinearity.
  arXiv  Detail & Related papers  (2024-06-24T14:50:05Z)
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• Unleashing the Power of Depth and Pose Estimation Neural Networks by Designing Compatible Endoscopic Images [12.412060445862842]
  We conduct a detail analysis of the properties of endoscopic images and improve the compatibility of images and neural networks. First, we introcude the Mask Image Modelling (MIM) module, which inputs partial image information instead of complete image information. Second, we propose a lightweight neural network to enhance the endoscopic images, to explicitly improve the compatibility between images and neural networks.
  arXiv  Detail & Related papers  (2023-09-14T02:19:38Z)
• Addressing caveats of neural persistence with deep graph persistence [54.424983583720675]
  We find that the variance of network weights and spatial concentration of large weights are the main factors that impact neural persistence. We propose an extension of the filtration underlying neural persistence to the whole neural network instead of single layers. This yields our deep graph persistence measure, which implicitly incorporates persistent paths through the network and alleviates variance-related issues.
  arXiv  Detail & Related papers  (2023-07-20T13:34:11Z)
• Increasing the Accuracy of a Neural Network Using Frequency Selective Mesh-to-Grid Resampling [4.211128681972148]
  We propose the use of keypoint frequency selective mesh-to-grid resampling (FSMR) for the processing of input data for neural networks. We show that depending on the network architecture and classification task the application of FSMR during training aids learning process. The classification accuracy can be increased by up to 4.31 percentage points for ResNet50 and the Oxflower17 dataset.
  arXiv  Detail & Related papers  (2022-09-28T21:34:47Z)
• Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks [61.60177890353585]
  Deep convolutional neural networks (CNNs) have been shown to provide excellent models for its functional analogue in the brain, the ventral stream in visual cortex. Here we consider some prominent statistical patterns that are known to exist in the internal representations of either CNNs or the visual cortex. We show that CNNs and visual cortex share a similarly tight relationship between dimensionality expansion/reduction of object representations and reformatting of image information.
  arXiv  Detail & Related papers  (2022-05-27T08:06:40Z)
• Biologically inspired deep residual networks for computer vision applications [0.0]
  We propose a biologically inspired deep residual neural network where the hexagonal convolutions are introduced along the skip connections. The proposed approach advances the baseline image classification accuracy of vanilla ResNet architectures.
  arXiv  Detail & Related papers  (2022-05-05T10:23:43Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Deep Spiking Convolutional Neural Network for Single Object Localization Based On Deep Continuous Local Learning [0.0]
  We propose a deep convolutional spiking neural network for the localization of a single object in a grayscale image. Results reported on Oxford-IIIT-Pet validates the exploitation of spiking neural networks with a supervised learning approach.
  arXiv  Detail & Related papers  (2021-05-12T12:02:05Z)
• Ventral-Dorsal Neural Networks: Object Detection via Selective Attention [51.79577908317031]
  We propose a new framework called Ventral-Dorsal Networks (VDNets) Inspired by the structure of the human visual system, we propose the integration of a "Ventral Network" and a "Dorsal Network" Our experimental results reveal that the proposed method outperforms state-of-the-art object detection approaches.
  arXiv  Detail & Related papers  (2020-05-15T23:57:36Z)

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.