Unifying Perplexing Behaviors in Modified BP Attributions through Alignment Perspective

• URL: http://arxiv.org/abs/2503.11160v1
• Date: Fri, 14 Mar 2025 07:58:26 GMT
• Title: Unifying Perplexing Behaviors in Modified BP Attributions through Alignment Perspective
• Authors: Guanhua Zheng, Jitao Sang, Changsheng Xu,
• Abstract summary: We present a unified theoretical framework for methods like GBP, RectGrad, LRP, and DTD.<n>We demonstrate that they achieve input alignment by combining the weights of activated neurons.<n>This alignment improves the visualization quality and reduces sensitivity to weight randomization.
• Score: 61.5509267439999
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Attributions aim to identify input pixels that are relevant to the decision-making process. A popular approach involves using modified backpropagation (BP) rules to reverse decisions, which improves interpretability compared to the original gradients. However, these methods lack a solid theoretical foundation and exhibit perplexing behaviors, such as reduced sensitivity to parameter randomization, raising concerns about their reliability and highlighting the need for theoretical justification. In this work, we present a unified theoretical framework for methods like GBP, RectGrad, LRP, and DTD, demonstrating that they achieve input alignment by combining the weights of activated neurons. This alignment improves the visualization quality and reduces sensitivity to weight randomization. Our contributions include: (1) Providing a unified explanation for multiple behaviors, rather than focusing on just one. (2) Accurately predicting novel behaviors. (3) Offering insights into decision-making processes, including layer-wise information changes and the relationship between attributions and model decisions.

Related papers

• A Meaningful Perturbation Metric for Evaluating Explainability Methods [55.09730499143998]
  We introduce a novel approach, which harnesses image generation models to perform targeted perturbation. Specifically, we focus on inpainting only the high-relevance pixels of an input image to modify the model's predictions while preserving image fidelity. This is in contrast to existing approaches, which often produce out-of-distribution modifications, leading to unreliable results.
  arXiv  Detail & Related papers  (2025-04-09T11:46:41Z)
• Faster Adaptive Optimization via Expected Gradient Outer Product Reparameterization [11.394969272703014]
  We show that for a broad class of functions, the sensitivity of adaptive algorithms to choice-of-basis is influenced by the decay of the EGOP matrix spectrum.
  arXiv  Detail & Related papers  (2025-02-03T18:26:35Z)
• Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression [7.369846475695131]
  We present an autoregressive SPO method directly targeting the optimization problem at the decision stage. We conduct experiments to demonstrate the effectiveness of the SPO+ surrogate compared to the absolute loss and the least squares loss.
  arXiv  Detail & Related papers  (2024-11-19T17:02:04Z)
• PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings [55.55445978692678]
  PseudoNeg-MAE is a self-supervised learning framework that enhances global feature representation of point cloud mask autoencoders. We show that PseudoNeg-MAE achieves state-of-the-art performance on the ModelNet40 and ScanObjectNN datasets.
  arXiv  Detail & Related papers  (2024-09-24T07:57:21Z)
• Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack [58.440711902319855]
  Edge perturbation is a method to modify graph structures. It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs) We propose a unified formulation and establish a clear boundary between two categories of edge perturbation methods.
  arXiv  Detail & Related papers  (2024-03-10T15:50:04Z)
• Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies [58.179981892921056]
  This work introduces a novel principle for disentanglement we call mechanism sparsity regularization. We propose a representation learning method that induces disentanglement by simultaneously learning the latent factors. We show that the latent factors can be recovered by regularizing the learned causal graph to be sparse.
  arXiv  Detail & Related papers  (2024-01-10T02:38:21Z)
• Input-gradient space particle inference for neural network ensembles [32.64178604645513]
  First-order Repulsive Deep Ensemble (FoRDE) is an ensemble learning method based on ParVI. Experiments on image classification datasets and transfer learning tasks show that FoRDE significantly outperforms the gold-standard DEs.
  arXiv  Detail & Related papers  (2023-06-05T11:00:11Z)
• Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective [72.55093886515824]
  We introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables. We devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a causal graph. Experiment results on synthetic and real datasets show that our three proposed components significantly improve the robustness and reusability of the learned motion representations.
  arXiv  Detail & Related papers  (2021-11-29T18:59:09Z)
• Variational Inference MPC using Tsallis Divergence [10.013572514839082]
  We provide a framework for Variational Inference-Stochastic Optimal Control by using thenon-extensive Tsallis divergence. A novel Tsallis Variational Inference-Model Predictive Control algorithm is derived.
  arXiv  Detail & Related papers  (2021-04-01T04:00:49Z)
• Contrastive Reasoning in Neural Networks [26.65337569468343]
  Inference built on features that identify causal class dependencies is termed as feed-forward inference. In this paper, we formalize the structure of contrastive reasoning and propose a methodology to extract a neural network's notion of contrast. We demonstrate the value of contrastively recognizing images under distortions by reporting an improvement of 3.47%, 2.56%, and 5.48% in average accuracy.
  arXiv  Detail & Related papers  (2021-03-23T05:54:36Z)
• Cogradient Descent for Bilinear Optimization [124.45816011848096]
  We introduce a Cogradient Descent algorithm (CoGD) to address the bilinear problem. We solve one variable by considering its coupling relationship with the other, leading to a synchronous gradient descent. Our algorithm is applied to solve problems with one variable under the sparsity constraint.
  arXiv  Detail & Related papers  (2020-06-16T13:41:54Z)

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.