Revealing Bias Formation in Deep Neural Networks Through the Geometric Mechanisms of Human Visual Decoupling

• URL: http://arxiv.org/abs/2502.11809v1
• Date: Mon, 17 Feb 2025 13:54:02 GMT
• Title: Revealing Bias Formation in Deep Neural Networks Through the Geometric Mechanisms of Human Visual Decoupling
• Authors: Yanbiao Ma, Bowei Liu, Wei Dai, Jiayi Chen, Shuo Li,
• Abstract summary: Deep neural networks (DNNs) often exhibit biases toward certain categories during object recognition. We propose a geometric analysis framework linking the geometric complexity of class-specific perceptual Manifolds to model bias. We present the Perceptual-Manifold-Geometry library, designed for calculating the geometric properties of perceptual Manifolds.
• Score: 9.609083308026786
• License:
• Abstract: Deep neural networks (DNNs) often exhibit biases toward certain categories during object recognition, even under balanced training data conditions. The intrinsic mechanisms underlying these biases remain unclear. Inspired by the human visual system, which decouples object manifolds through hierarchical processing to achieve object recognition, we propose a geometric analysis framework linking the geometric complexity of class-specific perceptual manifolds in DNNs to model bias. Our findings reveal that differences in geometric complexity can lead to varying recognition capabilities across categories, introducing biases. To support this analysis, we present the Perceptual-Manifold-Geometry library, designed for calculating the geometric properties of perceptual manifolds.

Related papers

• Geometry Distributions [51.4061133324376]
  We propose a novel geometric data representation that models geometry as distributions. Our approach uses diffusion models with a novel network architecture to learn surface point distributions. We evaluate our representation qualitatively and quantitatively across various object types, demonstrating its effectiveness in achieving high geometric fidelity.
  arXiv  Detail & Related papers  (2024-11-25T04:06:48Z)
• Exploring the Manifold of Neural Networks Using Diffusion Geometry [7.038126249994092]
  We learn manifold where datapoints are neural networks by introducing a distance between the hidden layer representations of the neural networks. These distances are then fed to the non-linear dimensionality reduction algorithm PHATE to create a manifold of neural networks. Our analysis reveals that high-performing networks cluster together in the manifold, displaying consistent embedding patterns.
  arXiv  Detail & Related papers  (2024-11-19T16:34:45Z)
• A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications [67.33002207179923]
  This paper presents a survey of data structures, models, and applications related to geometric GNNs. We provide a unified view of existing models from the geometric message passing perspective. We also summarize the applications as well as the related datasets to facilitate later research for methodology development and experimental evaluation.
  arXiv  Detail & Related papers  (2024-03-01T12:13:04Z)
• A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems [87.30652640973317]
  Recent advances in computational modelling of atomic systems represent them as geometric graphs with atoms embedded as nodes in 3D Euclidean space. Geometric Graph Neural Networks have emerged as the preferred machine learning architecture powering applications ranging from protein structure prediction to molecular simulations and material generation. This paper provides a comprehensive and self-contained overview of the field of Geometric GNNs for 3D atomic systems.
  arXiv  Detail & Related papers  (2023-12-12T18:44:19Z)
• Riemannian Residual Neural Networks [58.925132597945634]
  We show how to extend the residual neural network (ResNet) ResNets have become ubiquitous in machine learning due to their beneficial learning properties, excellent empirical results, and easy-to-incorporate nature when building varied neural networks.
  arXiv  Detail & Related papers  (2023-10-16T02:12:32Z)
• Experimental Observations of the Topology of Convolutional Neural Network Activations [2.4235626091331737]
  Topological data analysis provides compact, noise-robust representations of complex structures. Deep neural networks (DNNs) learn millions of parameters associated with a series of transformations defined by the model architecture. In this paper, we apply cutting edge techniques from TDA with the goal of gaining insight into the interpretability of convolutional neural networks used for image classification.
  arXiv  Detail & Related papers  (2022-12-01T02:05:44Z)
• Topology and geometry of data manifold in deep learning [0.0]
  This article describes and substantiates the geometric and topological view of the learning process of neural networks. We present a wide range of experiments on different datasets and different configurations of convolutional neural network architectures. Our work is a contribution to the development of an important area of explainable and interpretable AI through the example of computer vision.
  arXiv  Detail & Related papers  (2022-04-19T02:57:47Z)
• Generalized Shape Metrics on Neural Representations [26.78835065137714]
  We provide a family of metric spaces that quantify representational dissimilarity. We modify existing representational similarity measures based on canonical correlation analysis to satisfy the triangle inequality. We identify relationships between neural representations that are interpretable in terms of anatomical features and model performance.
  arXiv  Detail & Related papers  (2021-10-27T19:48:55Z)
• Statistical Mechanics of Neural Processing of Object Manifolds [3.4809730725241605]
  This thesis lays the groundwork for a computational theory of neuronal processing of objects. We identify that the capacity of a manifold is determined that effective radius, R_M, and effective dimension, D_M.
  arXiv  Detail & Related papers  (2021-06-01T20:49:14Z)
• A neural anisotropic view of underspecification in deep learning [60.119023683371736]
  We show that the way neural networks handle the underspecification of problems is highly dependent on the data representation. Our results highlight that understanding the architectural inductive bias in deep learning is fundamental to address the fairness, robustness, and generalization of these systems.
  arXiv  Detail & Related papers  (2021-04-29T14:31:09Z)
• Fusing the Old with the New: Learning Relative Camera Pose with Geometry-Guided Uncertainty [91.0564497403256]
  We present a novel framework that involves probabilistic fusion between the two families of predictions during network training. Our network features a self-attention graph neural network, which drives the learning by enforcing strong interactions between different correspondences. We propose motion parmeterizations suitable for learning and show that our method achieves state-of-the-art performance on the challenging DeMoN and ScanNet datasets.
  arXiv  Detail & Related papers  (2021-04-16T17:59:06Z)

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.