Title: AdaptGrad: Adaptive Sampling to Reduce Noise
Venue: NeurIPS 2025
Accepted: yes
Keywords: Explainable Artificial Intelligence

Neural Networks

Gradient Smoothing
Abstract: Gradient smoothing is an efficient approach to reducing noise in gradient-based model explanation methods. SmoothGrad adds Gaussian noise to mitigate much of this noise. However, the crucial hyperparameter in this method, the variance $\sigma$ of the Gaussian noise, is often set manually or determined using a heuristic approach. This results in the smoothed gradients containing extra noise introduced by the smoothing process. In this paper, we aim to analyze the noise and its connection to the out-of-range sampling in the smoothing process of SmoothGrad. Based on this insight, we propose AdaptGrad, an adaptive gradient smoothing method that controls out-of-range sampling to minimize noise. Comprehensive experiments, both qualitative and quantitative, demonstrate that AdaptGrad could effectively reduce almost all the noise in vanilla gradients compared to baseline methods. AdaptGrad is simple and universal, making it a practical solution to enhance gradient-based interpretability methods to achieve clearer visualization.


Peer reviews:
comments: SUMMARY:
The paper identifies a key limitation in SmoothGrad. SmoothGrad reduces noise in gradient-based saliency maps (e.g., Grad-CAM) by averaging gradients from inputs perturbed with Gaussian noise. However, its fixed noise variance hyperparameter causes out-of-range sampling, introducing extra noise into smoothed gradients. To address this, the authors propose AdaptGrad, an "adaptive" method that dynamically adjusts σ per input dimension. This ensures sampling stays within the valid data domain with a user-defined confidence level.

STRENGTHS AND WEAKNESSES:
Pros:
1. the paper is easy to follow;
2. it identifies some interesting limitations of SmoothGrade
3. it appears to be a practical and versatile XAI tool.

Cons:
1. The evaluation is not sufficent. First, very limited XAI methods are compared. Second, the metrics are insufficient and non-standard. some very common and established faithfulness metrics like Deletion/Insertion score and Pointing Game (for localization accuracy) are missing. Third, it neglects common XAI benchmarks like OpenXAI or Quantus.
2. The proposed method is very incremental to SmoothGrad without broader XAI implications, thus the novelty and significance of the method is very limited. It reads better suited as a workshop paper rather than NeurIPS, IMHO.
3. The table 1 shows very limited correlation.

QUESTIONS:
Please address the 3 weakness points;

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 1
ORIGINALITY: 2
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces AdaptGrad, a novel adaptive gradient smoothing method designed to reduce noise in gradient-based interpretability techniques such as SmoothGrad. By reinterpreting SmoothGrad as a convolution and analyzing its behavior, the authors identify a key source of “extra noise” caused by out-of-bound sampling. AdaptGrad addresses this by adaptively restricting the sampling range using confidence intervals based on data bounds. The method is theoretically motivated, computationally efficient, and empirically shown to improve saliency map quality across multiple models and interpretability methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n-The authors provide a clear theoretical reinterpretation of SmoothGrad using convolution, which leads to identifying a new source of noise (extra noise) and motivates the development of AdaptGrad.\n-AdaptGrad is conceptually straightforward, easy to implement, model-agnostic, and computationally efficient—making it broadly usable across existing gradient-based interpretability pipelines.\n- The method is evaluated using a wide range of metrics, datasets , models, and explanation methods , including indirect tasks such as object localization and adversarial robustness.\n\nWeaknesses\n- The evaluation of noise relies primarily on the Sparseness metric, which is not a direct measure of explanation quality or ground truth relevance, and may not capture all noise-related phenomena.\n-The evaluation is conducted on standard image classification tasks and does not demonstrate performance or applicability in more complex or high-stakes real-world domains\n\nQUESTIONS:\n1.  How sensitive is AdaptGrad’s performance to the choice of the confidence level, especially in domains with different data distributions or input scales?\n\n2.  Can AdaptGrad be effectively extended to non-vision domains (e.g., NLP or tabular data), where input bounds and semantics differ significantly?\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper studies a problem about generating visual saliency maps with better quality. A novel method named AdaptGrad is proposed to reduce the noise issue in gradient computation and improve the performance for better explanation.

STRENGTHS AND WEAKNESSES:
Strength:

1) The paper is well-organized and easy to follow. 

2) The related works are listed in detail with clear background, as well as the pros/cons. 

3) The analysis on SmoothGrad is interesting and motivated. The design of per-pixel variance in gradient computation is simple but novel. 
Experiment results show the effectiveness of the proposed method. The visual explanation map seems better than baseline methods. 

Weakness:

1) Experiments are limited. Only 1000 images from the ImageNet dataset is collected for evaluation. It is unsure about the stability and robustness of the proposed method  against different set of domain of images. No statistic significance is provided. 

2) Metrics are limited. Previous work usually use the AUC/deletion&insertion score to evaluate the quality of saliency map, while this work uses the sharpness and information level score, which are rare to use. 

3) The confidence is still required to manually tuned for better performance.

QUESTIONS:
What’s the difference of statistical behaviour when increasing N? How to decide an optimal N with good explanation quality but minimum computation cost?

The proposed method is pixel level-based computation. While the visual information is usuall redundant, it would be better to run the algorithm with group style.

LIMITATIONS:
See weakness and question. Authors also provide a discussion section about the limitation at the end of paper.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors investigate the problem of excessive noise when using the SmoothGrad feature attribution method. In this work, they propose a metric for quantifying the excess noise as well as a novel method AdaptGrad which limits the amount of excess noise based on a given threshold. AdaptGrad extends SmoothGrad by adaptively selecting the smoothing parameter $\\sigma$.  Quantitative and Qualitative experiments show that AdaptGrad reduces noise compared to SmoothGrad and other gradient-based attribution methods.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n* The method empirically seems to improve the sparseness and information level of explanations as compared with taking gradients or smoothgrad.\n* The proposed method is fast and simple to implement. It can be used in conjunction with other gradient-based methods.\n\n**Weaknesses:**\n* Manuscript could be made more rigorous and has issues with clarity (see \"other comments\" below).\n* While the AdaptGrad saliency maps seem to be more \"distinctive\" compared with SmoothGrad, it's not clear to me that the explanation is actually better. Since the smoothing parameter $\\sigma$ decreases for features that are closer to the boundary of [x_min, x_max] does this not simply highlight the dark / light pixels or pixels close to the boundary values?\n* No error bars or measures of variability in Table 2 and 3.\n* Limited theoretical contribution.\n\n**Other Comments**\n* The connection between SmoothGrad and convolutions / expectations (Section 3.1) is well known and used in previous works (e.g. proposition 1 in [1]). It's not clear if section 3 is intended to be background or part of this paper's contribution -- it would be helpful if this was more clearly delineated.\n* The paragraph under equation 6 (lines 116-121) is very confusing. In particular, \"the integral in Equation 4 which is not performed over $R^D$ but rather over a bounded domain\". However, Equation 4 is an indeed an integral over $R^D$ (or at least, it's not otherwise specified). The next line states that the domain of $G$ is defined over $\\Omega$ rather than $R^D$, however, this was not previously established in the paper (and the domain of $F$ is $R^D$).\n* In Equation 7, the notation $G(x_i + \\epsilon_i; x$ \\ $x_i)$ is not defined anywhere and conflicts with the previous definition of $G$.\n* Equation 10-12 is undefined when $x_i = x_{min}$ or  $x_{max}$\n* The symbol * indicates multiplication in Eq. 2 and convolution in Eq. 4, which is confusing.\n* It would be helpful if the metrics (e.g. consistency, invariance, sparseness) were defined in the main text, given that they are mentioned frequently. In addition, the definitions in the supplement could be made more formal. It's difficult to understand what each metric is measuring without referencing the original paper.\n* In Eq. 14, I believe the $x_i$ values need to be non-negative and sorted in non-decreasing order?\n\n[1] https://arxiv.org/pdf/2006.06643\n\nQUESTIONS:\n* How does the confidence interval defined in line 172 relate to AdaptGrad? This seems to be a range of values (which could be [0,0] if x_i = x_max) rather than relating to probability.\n* Did you explore simply hard thresholding the samples to be within [x_min, x_max], rather than adaptively reducing $\\sigma$? \n* What was the statistical test performed in Line 150? Do you have results for all experiment settings?\n\nLIMITATIONS:\nLimitations are discussed in section 6.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 1, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}