A Perception vs. Distortion Perspective on Score-Based Generative Channel Estimation

• URL: http://arxiv.org/abs/2606.16815v1
• Date: Mon, 15 Jun 2026 15:00:24 GMT
• Title: A Perception vs. Distortion Perspective on Score-Based Generative Channel Estimation
• Abstract summary: We present a theoretically grounded interpretation of score-based channel estimation.<n>We quantify the excess risk incurred by standard distortion-minimization approaches.<n>Under high predictive uncertainty, the large excess risk gap can be offset by score-based estimation.
• Score: 2.378977673869692
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Driven by their remarkable success in computer vision and inverse problem solving, score-based models are increasingly applied to wireless communications, where they show promise across a range of physical-layer tasks. However, despite this growing interest, the current literature often lacks a rigorous analysis of when score-matching offers a tangible advantage over traditional discriminative learning. This paper aims to address this gap through the use-case of channel estimation, a fundamental inverse problem in wireless systems. We present a theoretically grounded interpretation of score-based channel estimation through the lens of the perception-distortion tradeoff, identifying the conditions where score matching excels as well as its key limitations. In particular, by modeling downstream wireless tasks (e.g., capacity maximization) as functionals of the channel estimation process, we quantify the excess risk incurred by standard distortion-minimization approaches. Extensive numerical results show that under high predictive uncertainty, the large excess risk gap can be offset by score-based estimation, enabling near Bayesian-optimal precoding via the learned posterior, whereas in the low predictive uncertainty regime, discriminative distortion-minimization approaches are preferable due to lower complexity and more efficient use of model capacity.

Related papers

• Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference [48.78219918881965]
  We propose a principled framework for deriving task-specific strictly proper scoring rules by matching the local curvature of the downstream error metric.<n>We apply this to the Average Treatment Effect (ATE) estimation, deriving a closed-form loss and its corresponding canonical probability mapping.
  arXiv  Detail & Related papers  (2026-06-02T08:41:25Z)
• Possibilistic Predictive Uncertainty for Deep Learning [75.00607359322863]
  We introduce Dirichlet-approximated possibilistic posterior predictions (DAPPr)<n>DAPPr is a principled framework leveraging possibility theory.<n>This projection-and-approximation strategy yields a simple training objective with closed-form solutions.
  arXiv  Detail & Related papers  (2026-05-01T12:14:01Z)
• Embedding generalization within the learning dynamics: An approach based-on sample path large deviation theory [0.0]
  We consider an empirical risk perturbation based learning problem that exploits methods from continuous-time perspective. We provide an estimate in the small noise limit based on the Freidlin-Wentzell theory of large deviations. We also present a computational algorithm that solves the corresponding variational problem leading to an optimal point estimates.
  arXiv  Detail & Related papers  (2024-08-04T23:31:35Z)
• Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning [24.63284452991301]
  We propose a doubly robust causal effect estimator under networked interference. Specifically, we generalize the targeted learning technique into the networked interference setting. We devise an end-to-end causal effect estimator by transforming the identified theoretical condition into a targeted loss.
  arXiv  Detail & Related papers  (2024-05-06T10:49:51Z)
• Unmasking Bias in Diffusion Model Training [40.90066994983719]
  Denoising diffusion models have emerged as a dominant approach for image generation. They still suffer from slow convergence in training and color shift issues in sampling. In this paper, we identify that these obstacles can be largely attributed to bias and suboptimality inherent in the default training paradigm.
  arXiv  Detail & Related papers  (2023-10-12T16:04:41Z)
• Residual Error: a New Performance Measure for Adversarial Robustness [85.0371352689919]
  A major challenge that limits the wide-spread adoption of deep learning has been their fragility to adversarial attacks. This study presents the concept of residual error, a new performance measure for assessing the adversarial robustness of a deep neural network. Experimental results using the case of image classification demonstrate the effectiveness and efficacy of the proposed residual error metric.
  arXiv  Detail & Related papers  (2021-06-18T16:34:23Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)
• Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings [7.476901945542385]
  We show that Bayesian deep learning models on certain occasions marginally outperform conventional neural networks. Preliminary investigations indicate the potential inherent role of bias due to choices of initialisation, architecture or activation functions.
  arXiv  Detail & Related papers  (2020-09-03T16:58:15Z)
• Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions [121.10450359856242]
  We develop a frequentist procedure that utilizes influence functions of a model's loss functional to construct a jackknife (or leave-one-out) estimator of predictive confidence intervals. The DJ satisfies (1) and (2), is applicable to a wide range of deep learning models, is easy to implement, and can be applied in a post-hoc fashion without interfering with model training or compromising its accuracy.
  arXiv  Detail & Related papers  (2020-06-29T13:36:52Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)

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.