Title: DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Explainability

Activation Maximization

Diffusion Models

LLM
Abstract: Understanding and explaining the behavior of machine learning models is essential for building transparent and trustworthy AI systems. We introduce DEXTER, a data-free framework that employs 
diffusion models and large language models to generate global, textual explanations of visual classifiers. DEXTER operates by optimizing text prompts to synthesize class-conditional images that strongly activate a target classifier. These synthetic samples are then used to elicit detailed natural language reports that describe class-specific decision patterns and biases. Unlike prior work, DEXTER enables natural language explanation 
about a classifier's decision process without access to training data or ground-truth labels. We demonstrate DEXTER's flexibility across three tasks—activation maximization, slice discovery and debiasing, and bias explanation—each illustrating its ability to uncover the internal mechanisms of visual classifiers. Quantitative and qualitative evaluations, including a user study, show that DEXTER produces accurate, interpretable outputs. Experiments on ImageNet, Waterbirds, CelebA, and FairFaces confirm that DEXTER outperforms existing approaches in global model explanation and class-level bias reporting. Code is available at https://github.com/perceivelab/dexter.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a framework for generating ​global, textual explanations​ of visual classifiers without requiring training data. It first optimizes text prompts which are applied to generate class-specific images that maximally activate a target classifier. Then it utilizes large language models to analyze synthetic images to produce natural language reports on model behavior and decision patterns. The framework can be operated in tasks like activation maximization, slice discovery for debiasing and bias explanation.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Novel residual-like mask pseudo-labels prediction to support the integrated differentiable process for optimizing text prompts. The design can make joint optimization possible rather than treat prompt generation and image synthetis separately.\n2. Flexible application in various tasks. The method is applicable across tasks like bias detection, feature visualization, debiasing, which might be very useful in model performance improvement.\n3. Abundant and reasonable emprical design and results. Following multiple applicable tasks, it prepares sufficient qualitative and quantitative results to support claim well.\n\nWeaknesses:\n1. Unorganized method illustration. From section 3.1-3.3, in fact there are a lot of steps with complicated notations, K, V, agg, act, .etc, which is a huge chaos. Then it is hard to follow. Even if there is an overall loss function in section 3.3, it only works after certain pseudo-label has been set. So, the order and dependancy among different steps are unclear. A clear and integrated algorithm pipeline might be helpful.\n2. Insufficient details for some key steps. In section 3.3, for each pseudo-label $y_{i}$, its aggregated activation loss L_{agg, i} is initialized and optimized upon a subset of reference neurons $\\mathcal{N}_{i}$. It is unclear how the subset is defined or changed across the optimization process.\n\nQUESTIONS:\nI welcome the authors' comments on the concerns outlined in the weaknesses section of my review and their input to clarify any points I may have misunderstood.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces DEXTER, a framework for generating images that strongly activate targeted neurons in a given classifier. \nIts key components are :\n- a frozen Language Model (LM), pre-trained to perform Masked Language Modeling (MLM), which is used to fill in masked tokens in a template prompt, conditioned by a soft prompt vector;\n- a frozen CLIP text encoder that encodes the hard prompt completed by the LM;\n- a Stable Diffusion model that generates images conditioned by the embedding produced by the CLIP text encoder.\n\nThe LM’s soft prompt vector is the only trainable component in the pipeline.  \n\nThe words filled in by the LM can be used to identify spurious correlations learned by the classifier or for interpreting neural features. A bias report for the classifier can also be obtained by using a VLM to caption a set of generated images and then prompting the same VLM to analyse all the captions.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The framework is modular - the models used in the pipeline can be replaced with newer or specialized ones for domain-specific classifiers;\n2. It can perform bias or spurious correlation identification in a data-free manner - it does not require access to the training data of a classifier in order to identify spurious correlations that the classifier has learned;\n3. The framework’s effectiveness is proven in multiple tasks (activation maximization, slice discovery, bias explanation).\n\nWeaknesses:\n1. As noted by the authors, the framework is rather demanding in terms of hardware resources because it needs to perform backpropagation through the MLM encoder, the CLIP text encoder used to condition a diffusion model, the diffusion model itself and the targeted classifier;\n2. The initial objective (maximizing neural activations) is difficult to optimize, most likely due to the length of the backpropagation chain. To address this issue, the authors resorted to an auxiliary loss $\\mathcal{L}_{mask}$ added after the MLM encoder, which encourages the encoder to predict words that have historically led to higher activations in the classifier. Without this second loss term the results obtained in Table 4 are drastically lower for the multi-word prompt optimization;\n3. The diffusion model can generate additional objects besides what is expressed in the textual prompt, thus inducing some uncertainty with regards to what actually leads to an increased activation for a neuron - a concept mentioned in the textual prompt or a different one that is additionally generated by the diffusion model. This matters in the slice discovery application, but not as much for the bias explanation one, where the generated images are separately captioned and the bias report is generated from these extended captions; \n4. Each component in the pipeline limits the concepts that the framework can identify as important for the classifier (words that the MLM encoder tends to predict in the template prompt and are also in the vocabulary of CLIP’s text encoder, or concepts that the diffusion model can generate), but this can be addressed by upgrading any lacking component (assuming that an appropriate replacement exists)\n\nQUESTIONS:\nQuestions\n\n1. Are the activation scores in Tables 3 and 4 the mean activations for the correct class across multiple images? If so, could the authors also provide the corresponding standard deviations?\n\n2. In Appendix C how are the top-k words from Table 7 selected? Is the pipeline run multiple times and the top word of each run is selected (the last pseudo-target of $\\mathcal{L}_{mask}$), or do the author select the top-k words based on the activations produced in a single run?\n\n3. For the words in Table 7, could the authors provide the scores used to rank them, as well as the frequency at which they were sampled in the optimization process? (this is related to question 2 and the discussion below)\n\nWhile the method can be used to identify spurious correlations (SCs) learned by the classifier (the experiments on the Waterbirds and CelebA datasets), the diversity of the proposed words seems a bit low (Appendix Table 7), at least when compared to methods such as B2T[1], Lg[2] or WASP[3]. This is not a major issue, as the identification of SCs is not the main goal of DEXTER, unlike the previously mentioned works. \n\nHowever, I was wondering if qualitative improvements could be made in this regard by simply selecting more words sampled from the MLM encoder in the optimization process. For example, by ranking the sampled words by the strength of the produced activations in the classifier and presenting them in this order, together with the number of times they were sampled from the MLM encoder. This information should be freely available and I think that it would make for an interesting addition to the Appendix. \n\nIt could also offer more insights on how the optimization process works - does it explore multiple valid SCs in this setup and then converges to one of them, or does it simply lock onto the first SC that it finds? A slight concern would be that the mask pseudo-label loss ($\\mathcal{L}_{mask}$) might limit the exploration of potential SCs. \n\nThis type of analysis could also be done for the experiment in Appendix G.1, where the pseudo-label is initialized with the wrong class.\n\nSuggestions:\n- The results in Section 4.2 are a bit hard to parse - e.g. the caption of Table 3 doesn’t seem to offer any sort of hint as to what those numbers represent. I think the clarity of the presented results should be improved in this regard.\n- There are a few typos that should be fixed  - $\\mathcal{L}$mask, on page 9, lines 319 and 322.\n\n[1] Kim et al. \"Discovering and mitigating visual biases through keyword explanation.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.\n\n[2] Zhao et al. \"Language-guided detection and mitigation of unknown dataset bias.\" arXiv preprint arXiv:2406.02889 (2024).\n\n[3] Paduraru et al. “WASP: A Weight-Space Approach to Detecting Learned Spuriousness.” arXiv preprint arXiv:2410.18970 (2024).\n\nLIMITATIONS:\nThe authors analysed the limitations of their method in Section 5.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a novel, data-free framework, DEXTER, to generate global multimodal explanations for vision classifiers. It leverages diffusion models for image generation and large language models (LLMs) for textual reasoning, providing interpretability without requiring training data or labels. The paper evaluates DEXTER on multiple interpretability tasks including activation maximization, slice discovery, and bias explanation, using standard datasets like ImageNet, Waterbirds, CelebA, and FairFaces.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n### Contribution is novel\n- DEXTER that extracts global, class-wise or feature neuron model explanations without any access to data or labels is highly novel. This advances the field of post-hoc interpretability significantly.\n- DEXTER generates multimodal explanation combining text explanation, textual reasoning, and visual synthesis. This advances the explanations more human-interpretable than existing visual-only or textual-only approaches.\n### Thorough Evaluation and Benchmarks\n- DEXTER was evaluated on four well-motivated tasks using four corresponding datasets.\n- Performance is validated with quantitative and qualitative metrics, user studies, and ablation studies. \n### Correctness\n- To the best of my knowledge, the proposal appears to be sound. Although DEXTER includes foundational models like BERT, StableDiffusion, and CLIP, but the whole process was designed such novel and well that the whole process is fully differentiable allowing to optimize soft prompt p which is the only learnable parameter. \n\n## Weaknesses:\n### Proposal's presentation can be improved\n- The paper lacks of a formalization of generating bias reasoning and slice discovery. Bias reasoning and slide discovery are illustrated in Figure 1 as main contributions of the paper beside DEXTER. However, their current descriptions seem to be dataset-dependent as they are given only along with specific datasets in the Section Performance Analysis. Also, Slice Discovery was experimented only with class neuron? This will raise the question whether Slice Discovery can be applied for feature neuron or not?\n- The selection of P in soft prompts was neither discussed nor analyzed. The advantages/disadvantages of P > 1 compared with P = 1 demand futher analyses.\n- There is no discussion on how to determine key neural features efficiently. The paper only provided use cases where neural features are already included in the used dataset SalientImageNet.\n- d(e) used in the formula n = f(d(e)) demonstrated that the diffusion model is applied one time per optimization step during training (i.e., the number of inference step for Stable Diffusion is 1). Is this correct? This demands further clarification.\n- The design choice of loss function in Formula (2) needs discussion. Is that because if n_i is class neuron, 0 <= n_i < 1, the corresponding loss is at logarithm scale, and if n_i is feature neuron, n_i can be much greater, thus the corresponding loss is at linear scale?\n- The paper did not discuss the potential issues of the randomness in each stable diffusion's inference step i.e., d(e) during training.\n- o_i^{(C)} = Mo_i seems not correct. Should it be o_i^{(C)} = o_i M?\n- The experimentation results showed that DEXTER prefers conceptual while DiffExplainer prefers perceptual. However, it is yet unclear for which of their design differences attributed for those observations.\n- Formula (4) is not well-defined. The uses of subscriptions j and i in \\Sum_{j=1}^N log s_{i, y_i} are confused. \n- How can Semantic CLIP-IQA achieves higher score than CLIP-IQA? Provide intuition. Does this mean some images are good photo of [CLASS] but is not a good photo?\n\n### Additional limitations of the proposal\n- **Dependence on foundation models:** Another possible limitation is that DEXTER requires VLM and Stable Diffusion need to have conceptual/semantics understanding of common objects. For example, when investigating chest-X-ray-image related classes but Stable Diffusion was not pretrained with Chest X-ray images. So \n- **Generalization to other complex tasks or other visual classifiers:**\n  + The current evaluation only experimented with one visual classifier, RobustResNet50, which fails to demonstrate the ability to generalize DEXTER performance on other visual classifiers. \n  + The paper focuses on image classification models. It is unclear how well the approach would generalize to detection, segmentation, or multimodal tasks involving reasoning across modalities beyond vision and text.\n  + The pipeline is customized for class-wise analysis, which may not scale well to instance-level explainability in domains like medical imaging.\n\n### Other minor comments:\n- To the best of my knowledge, the target class should be one of the best pseudo-lables returned, and such pseudo-labels are not of interests. How to avoid this? Considering the example in Figure 1 where the investigation class is `bee`, how DEXTER avoids return the pseudo-labels as `bee` which is not of interests.\n- The claim `DiffExplainer [31] introduced diffusion-based image generation guided by soft prompts, improving realism but at the cost of interpretability: soft prompts are continuous vectors with no semantic transparency. ` is not precise. Indeed, [31] includes section 4.4 `towards text explanations` to instruct how to provide textual explanations (eventhough this was not fully investigated in the paper)\n- The textual explanations rely on LLM captions and summarization, which may introduce hallucinations—although mitigated, this remains a concern.\n\nQUESTIONS:\n1- During training, in each optimization step, how many inference steps were taken for LCM? Is it just 1 inference step?\n2- How can Semantic CLIP-IQA achieves higher score than CLIP-IQA? Provide intuition. Does this mean some images are good photo of [CLASS] but is not a good photo?\n3- Can you please provide comments on how to efficiently select feature neurons if they are not provided in a dataset?\n\nLIMITATIONS:\n### Possible additional limitations of the proposal\n- **Dependence on foundation models:** Another possible limitation is that DEXTER requires VLM and Stable Diffusion need to have conceptual/semantics understanding of common objects. For example, when investigating chest-X-ray-image related classes but Stable Diffusion was not pretrained with Chest X-ray images. So \n- **Generalization to other complex tasks or other visual classifiers:**\n  + The current evaluation only experimented with one visual classifier, RobustResNet50, which fails to demonstrate the ability to generalize DEXTER performance on other visual classifiers. \n  + The paper focuses on image classification models. It is unclear how well the approach would generalize to detection, segmentation, or multimodal tasks involving reasoning across modalities beyond vision and text.\n  + The pipeline is customized for class-wise analysis, which may not scale well to instance-level explainability in domains like medical imaging.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-18"}