Automated Natural Language Explanation of Deep Visual Neurons with Large Models

• URL: http://arxiv.org/abs/2310.10708v1
• Date: Mon, 16 Oct 2023 17:04:51 GMT
• Title: Automated Natural Language Explanation of Deep Visual Neurons with Large Models
• Authors: Chenxu Zhao, Wei Qian, Yucheng Shi, Mengdi Huai, Ninghao Liu
• Abstract summary: This paper proposes a novel post-hoc framework for generating semantic explanations of neurons with large foundation models. Our framework is designed to be compatible with various model architectures and datasets, automated and scalable neuron interpretation.
• Score: 43.178568768100305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks have exhibited remarkable performance across a wide range of real-world tasks. However, comprehending the underlying reasons for their effectiveness remains a challenging problem. Interpreting deep neural networks through examining neurons offers distinct advantages when it comes to exploring the inner workings of neural networks. Previous research has indicated that specific neurons within deep vision networks possess semantic meaning and play pivotal roles in model performance. Nonetheless, the current methods for generating neuron semantics heavily rely on human intervention, which hampers their scalability and applicability. To address this limitation, this paper proposes a novel post-hoc framework for generating semantic explanations of neurons with large foundation models, without requiring human intervention or prior knowledge. Our framework is designed to be compatible with various model architectures and datasets, facilitating automated and scalable neuron interpretation. Experiments are conducted with both qualitative and quantitative analysis to verify the effectiveness of our proposed approach.

Related papers

• Retinal Vessel Segmentation via Neuron Programming [17.609169389489633]
  This paper introduces a novel approach to neural network design, termed neuron programming'', to enhance a network's representation ability at the neuronal level. Comprehensive experiments validate that neuron programming can achieve competitive performance in retinal blood segmentation.
  arXiv  Detail & Related papers  (2024-11-17T16:03:30Z)
• Adapting the Biological SSVEP Response to Artificial Neural Networks [5.4712259563296755]
  This paper introduces a novel approach to neuron significance assessment inspired by frequency tagging, a technique from neuroscience. Experiments conducted with a convolutional neural network for image classification reveal notable harmonics and intermodulations in neuron-specific responses under part-based frequency tagging. The proposed method holds promise for applications in network pruning, and model interpretability, contributing to the advancement of explainable artificial intelligence.
  arXiv  Detail & Related papers  (2024-11-15T10:02:48Z)
• Interpreting Neural Networks through Mahalanobis Distance [0.0]
  This paper introduces a theoretical framework that connects neural network linear layers with the Mahalanobis distance. Although this work is theoretical and does not include empirical data, the proposed distance-based interpretation has the potential to enhance model robustness, improve generalization, and provide more intuitive explanations of neural network decisions.
  arXiv  Detail & Related papers  (2024-10-25T07:21:44Z)
• Statistical tuning of artificial neural network [0.0]
  This study introduces methods to enhance the understanding of neural networks, focusing specifically on models with a single hidden layer. We propose statistical tests to assess the significance of input neurons and introduce algorithms for dimensionality reduction. This research advances the field of Explainable Artificial Intelligence by presenting robust statistical frameworks for interpreting neural networks.
  arXiv  Detail & Related papers  (2024-09-24T19:47:03Z)
• Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
  We propose a biologically-informed framework for enhancing artificial neural networks (ANNs) Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors. We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
  arXiv  Detail & Related papers  (2024-07-05T14:11:28Z)
• Manipulating Feature Visualizations with Gradient Slingshots [54.31109240020007]
  We introduce a novel method for manipulating Feature Visualization (FV) without significantly impacting the model's decision-making process. We evaluate the effectiveness of our method on several neural network models and demonstrate its capabilities to hide the functionality of arbitrarily chosen neurons.
  arXiv  Detail & Related papers  (2024-01-11T18:57:17Z)
• 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)
• Learning to Act through Evolution of Neural Diversity in Random Neural Networks [9.387749254963595]
  In most artificial neural networks (ANNs), neural computation is abstracted to an activation function that is usually shared between all neurons. We propose the optimization of neuro-centric parameters to attain a set of diverse neurons that can perform complex computations.
  arXiv  Detail & Related papers  (2023-05-25T11:33:04Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Spiking neural network for nonlinear regression [68.8204255655161]
  Spiking neural networks carry the potential for a massive reduction in memory and energy consumption. They introduce temporal and neuronal sparsity, which can be exploited by next-generation neuromorphic hardware. A framework for regression using spiking neural networks is proposed.
  arXiv  Detail & Related papers  (2022-10-06T13:04:45Z)

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.