{"ID":2866098,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03245","arxiv_id":"2510.03245","title":"Frequency-Aware Model Parameter Explorer: A new attribution method for improving explainability","abstract":"State-of-the-art attribution methods rely on adversarial sample generation that applies an all-pass filter across the frequency spectrum, discarding fine-grained high-frequency information that is demonstrably important for accurate feature attribution in deep neural networks. By generating adversarial samples that selectively perturb high- and low-frequency components, we can probe which spectral features a model relies on most -- directly translating frequency-domain exploration into attribution signals. Building on this insight, we propose FAMPE (Frequency-Aware Model Parameter Explorer), a novel attribution method that introduces an FFT-based α-weighted perturbation scheme -- separately modulating high- and low-frequency components via an energy-driven spectral cutoff -- and, crucially, integrates this frequency-aware exploration directly into model parameter exploration for attribution, a connection that has not been established in prior work. Unlike prior frequency-aware adversarial approaches that target transferability or imperceptibility, FAMPE's specific formulation is designed and validated exclusively for explainability, translating spectral structure into fine-grained attribution maps without requiring any manual baseline selection. Evaluated on ImageNet across four architectures spanning CNNs and Vision Transformers, at fixed α= 0.1 FAMPE outperforms AttEXplore by 4.25% on Inception-v3 and 12.04% on MaxViT-T, with per-sample oracle selection further revealing that low-frequency-dominated images systematically benefit from high-frequency perturbations -- underscoring the potential of adaptive spectral exploration. Our ablation studies confirm that high-frequency perturbations are disproportionately responsible for attribution precision, while excessive low-frequency noise degrades global structural coherence.","short_abstract":"State-of-the-art attribution methods rely on adversarial sample generation that applies an all-pass filter across the frequency spectrum, discarding fine-grained high-frequency information that is demonstrably important for accurate feature attribution in deep neural networks. By generating adversarial samples that sel...","url_abs":"https://arxiv.org/abs/2510.03245","url_pdf":"https://arxiv.org/pdf/2510.03245v2","authors":"[\"Ali Yavari\",\"Alireza Mohamadi\",\"Elham Beydaghi\",\"Philipp Seeböck\",\"Rainer A. Leitgeb\"]","published":"2025-09-25T15:00:44Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CV\"]","methods":"[\"Vision Transformer\",\"Transformer\",\"LoRA\",\"Convolutional Neural Network\"]","has_code":false}