Fourier Feature Attribution: A New Efficiency Attribution Method

• URL: http://arxiv.org/abs/2504.02016v1
• Date: Wed, 02 Apr 2025 13:20:19 GMT
• Title: Fourier Feature Attribution: A New Efficiency Attribution Method
• Authors: Zechen Liu, Feiyang Zhang, Wei Song, Xiang Li, Wei Wei,
• Abstract summary: We propose a novel Fourier feature attribution method grounded in signal decomposition theory.<n>Our experiments show that Fourier feature attribution exhibits superior feature selection capabilities compared to spatial domain attribution methods.<n>Results reveal that Fourier features exhibit greater intra-class concentration and inter-class distinctiveness, indicating their potential for more efficient classification and explainable AI algorithms.
• Score: 11.76411086670363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The study of neural networks from the perspective of Fourier features has garnered significant attention. While existing analytical research suggests that neural networks tend to learn low-frequency features, a clear attribution method for identifying the specific learned Fourier features has remained elusive. To bridge this gap, we propose a novel Fourier feature attribution method grounded in signal decomposition theory. Additionally, we analyze the differences between game-theoretic attribution metrics for Fourier and spatial domain features, demonstrating that game-theoretic evaluation metrics are better suited for Fourier-based feature attribution. Our experiments show that Fourier feature attribution exhibits superior feature selection capabilities compared to spatial domain attribution methods. For instance, in the case of Vision Transformers (ViTs) on the ImageNet dataset, only $8\%$ of the Fourier features are required to maintain the original predictions for $80\%$ of the samples. Furthermore, we compare the specificity of features identified by our method against traditional spatial domain attribution methods. Results reveal that Fourier features exhibit greater intra-class concentration and inter-class distinctiveness, indicating their potential for more efficient classification and explainable AI algorithms.

Related papers

• Robustifying Fourier Features Embeddings for Implicit Neural Representations [25.725097757343367]
  Implicit Neural Representations (INRs) employ neural networks to represent continuous functions by mapping coordinates to the corresponding values of the target function.<n>INRs face a challenge known as spectral bias when dealing with scenes containing varying frequencies.<n>We propose the use of multi-layer perceptrons (MLPs) without additive.
  arXiv  Detail & Related papers  (2025-02-08T07:43:37Z)
• Robust Fourier Neural Networks [1.0589208420411014]
  We show that introducing a simple diagonal layer after the Fourier embedding layer makes the network more robust to measurement noise. Under certain conditions, our proposed approach can also learn functions that are noisy mixtures of nonlinear functions of Fourier features.
  arXiv  Detail & Related papers  (2024-09-03T16:56:41Z)
• Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation [53.91958614666386]
  Unsupervised graph representation learning (UGRL) based on graph neural networks (GNNs) We propose a novel UGRL method based on Multi-hop feature Quality Estimation (MQE)
  arXiv  Detail & Related papers  (2024-07-29T12:24:28Z)
• Fourier Sensitivity and Regularization of Computer Vision Models [11.79852671537969]
  We study the frequency sensitivity characteristics of deep neural networks using a principled approach. We find that computer vision models are consistently sensitive to particular frequencies dependent on the dataset, training method and architecture.
  arXiv  Detail & Related papers  (2023-01-31T10:05:35Z)
• Deep Fourier Up-Sampling [100.59885545206744]
  Up-sampling in the Fourier domain is more challenging as it does not follow such a local property. We propose a theoretically sound Deep Fourier Up-Sampling (FourierUp) to solve these issues.
  arXiv  Detail & Related papers  (2022-10-11T06:17:31Z)
• Fourier Disentangled Space-Time Attention for Aerial Video Recognition [54.80846279175762]
  We present an algorithm, Fourier Activity Recognition (FAR), for UAV video activity recognition. Our formulation uses a novel Fourier object disentanglement method to innately separate out the human agent from the background. We have evaluated our approach on multiple UAV datasets including UAV Human RGB, UAV Human Night, Drone Action, and NEC Drone.
  arXiv  Detail & Related papers  (2022-03-21T01:24:53Z)
• Aspect Based Sentiment Analysis Using Spectral Temporal Graph Neural Network [0.0]
  complexity of the review sentences, presence of double negation and specific usage of words make it difficult to predict the sentiment accurately. We propose graph Fourier transform based network with features created in the spectral domain. Our proposed model also found competitive results on the two other recently proposed datasets from the e-commerce domain.
  arXiv  Detail & Related papers  (2022-02-14T14:55:00Z)
• Functional Regularization for Reinforcement Learning via Learned Fourier Features [98.90474131452588]
  We propose a simple architecture for deep reinforcement learning by embedding inputs into a learned Fourier basis. We show that it improves the sample efficiency of both state-based and image-based RL.
  arXiv  Detail & Related papers  (2021-12-06T18:59:52Z)
• A Fourier-based Framework for Domain Generalization [82.54650565298418]
  Domain generalization aims at tackling this problem by learning transferable knowledge from multiple source domains in order to generalize to unseen target domains. This paper introduces a novel Fourier-based perspective for domain generalization. Experiments on three benchmarks have demonstrated that the proposed method is able to achieve state-of-the-arts performance for domain generalization.
  arXiv  Detail & Related papers  (2021-05-24T06:50:30Z)
• Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases [73.53227696624306]
  We present a new family of algorithms for learning Fourier-sparse set functions. In contrast to other work that focused on the Walsh-Hadamard transform, our novel algorithms operate with recently introduced non-orthogonal Fourier transforms. We demonstrate effectiveness on several real-world applications.
  arXiv  Detail & Related papers  (2020-10-01T14:31:59Z)

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.