CoSy: Evaluating Textual Explanations of Neurons

Authors: laura  kopf
philine lou bommer
anna  hedström
sebastian  lapuschkin
-c  höhne
kirill  bykov
t u berlin
  bifold
  germany
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-15 09:15:14

Abstract: A crucial aspect of understanding the complex nature of Deep Neural Networks (DNNs) is the ability to explain learned concepts within their latent representations. While methods exist to connect neurons to human-understandable textual descriptions, evaluating the quality of these explanations is challenging due to the lack of a unified quantitative approach. We introduce CoSy (Concept Synthesis), a novel, architecture-agnostic framework for evaluating textual explanations of latent neurons. Given textual explanations, our proposed framework uses a generative model conditioned on textual input to create data points representing the explanations. By comparing the neuron's response to these generated data points and control data points, we can estimate the quality of the explanation. We validate our framework through sanity checks and benchmark various neuron description methods for Computer Vision tasks, revealing significant differences in quality.

Main paper: 
 introduction: 
 One of the key obstacles to the wider adoption of Machine Learning methods across various fields is the inherent opacity of modern Deep Neural Networks (DNNs)-in essence, we often lack an understanding of why these models make certain predictions. To address this problem, various explainability methods [1, 2] have been developed to make the decision-making processes of DNNs more understandable to humans. Explainability methods have broadened their focus from interpreting the decision-making of DNNs locally-for instance, interpreting specific inputs using saliency maps [3, 4, 5, 6] -to understanding the global behavior of models by analyzing individual model components and their functional purpose [7] . Following the latter global explainability approach, often referred to as mechanistic interpretability [8, 9, 10] , some methods aim to describe the specific concepts neurons have learned to detect [11, 12, 13, 14, 15, 16] , enabling analysis of how these high-level concepts influence network predictions.A popular approach for explaining the functionality of latent representations of a network is to describe neurons using human-understandable textual concepts. A textual description is assigned to a neuron based on the concepts that the neuron has learned to detect or is significantly activated by. Over time, these methods have evolved from providing label-specific descriptions [11] to more complex compositional [12, 16] and open-vocabulary explanations [13, 15] . However, a significant challenge remains: the lack of a universally accepted quantitative evaluation measure for open-vocabulary neuron descriptions. As a consequence, different methods devised their own evaluation criteria, making it difficult to perform general-purpose, comprehensive cross-comparisons. Figure 1: A schematic illustration of the COSY evaluation framework for Neuron 80 in ResNet18's avgpool layer. The current challenge lies in the absence of general-purpose, quantitative evaluation measures to benchmark textual explanations of neurons. To address this, we propose COSY, a framework consisting of three steps: first, a generative model translates textual concepts into the visual domain, creating synthetic images for each explanation using a text-to-image model. Then, inference is performed on these synthetic images alongside a control image dataset to collect neuron activations. Finally, by comparing activations from the synthetic images with those from the control dataset, we quantitatively assess the quality of the textual explanation and compare results across different explanation methods. The implementation details of this example can be found in Appendix A.2.In this work, we aim to bridge this gap by introducing Concept Synthesis (COSY), the first automatic evaluation framework for textual explanations of neurons in Computer Vision (CV) models (illustrated in Figure 1 ). Our approach builds on recent advancements in Generative AI, which enable the generation of synthetic images that align with provided neuron explanations. We use a set of available text-to-image models to synthesize data points that are prototypical for specific target explanations. These data points allow us to evaluate how neurons differentiate between concept-related images and non-concept-related images combined in a control dataset. We summarize our contributions as below:(C1) We provide the first general-purpose, quantitative evaluation framework COSY (Section 3) that enables the evaluation of individual or a set of textual explanation methods for CV models. (C2) In a series of sanity checks (Section 4), we analyze the choice of generative models and prompts for synthetic image generation, demonstrating framework reliability. (C3) We benchmark existing explanation methods (Section 5) and extract novel insights, revealing substantial variability in the quality of explanations. Generally, textual explanations for lower layers are less accurate compared to those for higher layers. 
related works: 
 Activation Maximization Activation Maximization is a commonly used methodology to understand what a neuron has learned to detect [17] . Such methods work by identifying input signals that trigger the highest activation in a neuron. This can be achieved synthetically, where an optimization process is employed to create the optimal input that maximizes the neuron's activation [18, 19, 20] , or naturally, by finding such inputs within a data corpus [21] . Activation Maximization has been employed for explaining latent representations of models [22, 23] , including probabilistic models [24] , detection of backdoor attacks [25] and spurious correlations [26] . However, one of the key limitations of this methodology lies in its inability to scale, as it relies on users to manually audit maximization signals.Automatic Neuron Interpretation A more scalable alternative approach involves linking neurons with human-understandable concepts through textual descriptions. Network Dissection [11] (NetDissect) is a pioneering method in this field, associating convolutional neurons with a concept based on the Intersection over Union (IoU) of neuron activation maps and ground truth segmentation masks.Table 1 : Comparison of characteristics of neuron description methods. The columns (from left to right) represent the explanation method used, its textual output type (fixed-label, compositional, or open-vocabulary), the type of neuron targeted for analysis (convolutional, scalar, or predetermined), the target metric the method optimizes (IoU, WPMI, AUC, etc.), whether the method relies on auxiliary black-box models for finding or generating explanations (img2txt model, CLIP), and whether the explanation method is architecture-agnostic, meaning it can be applied to any CV model. For a more detailed description of each method, refer to Appendix A.1.Neuron Type Target Black-Box Dependency Architecture-AgnosticNetDissect [11] fixed-label conv. IoU -✓ CompExp [12] compositional conv. IoU -✓ MILAN [13] open-vocabulary conv. WPMI img2txt model ✓ FALCON [14] open-vocabulary predetermined avg. CLIP score CLIP -CLIP-Dissect [15] open-vocabulary scalar SoftWPMI CLIP ✓ INVERT [16] compositional scalar AUC -✓Building on this, Compositional Explanations of Neurons (CompExp) [12] enhanced explanation detail by enabling the use of compositional concepts-i.e., concepts constructed with logical operators. MILAN [13] further expanded this by allowing for open-vocabulary explanations, permitting the generation of descriptions beyond predefined labels. INVERT [16] adopted a compositional concept approach, enabling explanations for general neuron types without the need for segmentation masks. It assigns compositional labels based on a neuron's ability to distinguish concepts, using the Area Under the Receiver Operating Characteristic Curve (AUC). FALCON [14] and CLIP-Dissect [15] compute image-text similarity with a CLIP model [27] for the most activating images and their corresponding captions or concept sets. Each method defines its optimization criteria, lacking a unified consensus on what constitutes a good explanation. For detailed descriptions of the methods and their optimization objectives, please refer to Appendix A.1. An overview of the different techniques is illustrated in Table 1 .Prior Methods for Evaluation While significant effort has been made towards developing approaches and tools for evaluating local explanations [28, 29, 30] , there has been relatively limited focus on evaluating global methods, in particular neuron description methods. Currently, to the best of our knowledge, there is no unified approach that allows for benchmarking across models and explanation methods. In their respective papers, the INVERT and CLIP-Dissect explanation methods evaluated the accuracy of their explanations by comparing the generated neuron labels with ground truth descriptions provided for neurons in the output layer of a network. However, this evaluation is limited to output neurons and fixed labels only. CLIP-Dissect additionally evaluates the quality of explanations by computing the Cosine Similarity in a sentence embedding space between the ground truth class name for each neuron and the explanation generated by the method. FALCON employs a human study conducted on Amazon Mechanical Turk to evaluate the concepts generated by the method. Participants are tasked with selecting the best explanation for each target feature from a selection of explanation methods, considering a given set of highly and lowly activating images. MILAN evaluates the performance of neuron labeling methods relative to human annotations using BERTScores [31] . While human studies are generally beneficial, the conventional setup can be misleading and may fail to fully capture the intended evaluation criteria, introducing potential biases. Typically, annotators describe the images that most strongly activate a neuron, and these descriptions are then compared to an automatic explanation. However, this approach primarily evaluates the alignment with the most activating images rather than the accuracy of the explanation in describing the neuron's function. Moreover, these highly activating images may not accurately represent the neuron's overall behavior, as they only reflect the maximum tail of the distribution.Question: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes]Justification: We mention limitations throughout the text and discuss them further in Paragraph 6Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
preliminaries: 
 Consider a Deep Neural Network (DNN) represented by the function g : X → Z, where X ⊂ R h×w×c denotes the input image domain and Z ⊂ R l represents the model's output domain. We can view the model as a composition of two functions, F : X → Y, and L : Y → Z, such that g = L • F.Here Y ⊂ R d×w * ×h * , where d ∈ N is the number of neurons in the layer, and w * , h * ∈ N represent the width and height of the feature map, respectively. The function F, which we refer to as the feature extractor, can be chosen based on the layer of the model we aim to inspect. This could be an existing layer within the model or a concept bottleneck layer [32] . We refer to the i-th neuron within the layer as f i (x) = F i (x) : X → R w * ×h * . Within the scope of this paper, we refer to explanation method as an operator E that maps a neuron to the textual description s = E(f i ) ∈ S, where S is a set of potential textual explanations. The specific set of explanations depends on the implementation of the particular method (see Appendix A.1).We assume that a good textual explanation for a neuron should provide a human-understandable description of an input that strongly activates the neuron. However, modern methods for explaining the functional purpose of neurons often provide open-vocabulary textual explanations, complicating the quantitative collection of natural data that represents the explanation. To address this issue, COSY utilizes recent advancements in generative models to synthesize data points that correspond to the textual explanation. The response of a neuron to a set of synthetic images is measured and compared to the neuron's activation on a set of control natural images representing random concepts. This comparison allows for a quantitative evaluation of the alignment between the explanation and the target neuron.The parameters of the proposed method include a control dataset X 0 = {x 0 1 , . . . , x 0 n } ⊂ X , n ∈ N, which consists of natural images representing the concepts on which the model was originally trained. Additionally, it incorporates a generative model p M used for synthesizing images, along with a specified number of generated images m ∈ N. The control dataset typically includes a balanced selection of validation class images. Given a neuron f i and explanation s ∈ S, COSY evaluates the alignment between the explanation and a neuron in three consecutive steps, which are illustrated in Figure 1 .1. Generate Synthetic Data. The first step involves generating synthetic images for a given explanation s ∈ S, which we use as a prompt to a generative model p M to create a collection of synthetic images, denoted asX 1 = {x 1 1 , . . . , x 1 m } ∼ p M (x | s). This collection consists of m ∈ N images, where m is adjustable as a parameter of the evaluation procedure. 2. Collect Neuron Activations. Given the control dataset X 0 and the set of generated synthetic images X 1 , we collect activations as follows:EQUATIONwhere σ : R w * ×h * → R is an aggregation function for multi-dimensional neurons. Within the scope of our paper, we use Average Pooling as aggregation functionEQUATION3. Score Explanations. The final step of the proposed method relies on the evaluation of the difference between neuron activations on the control dataset A 0 and neuron activations given the synthetic dataset A 1 . To quantify this difference, we utilize a scoring function Ψ : R n × R m → R to measure the difference between the distributions of activations.In the context of our paper, we employ the following scoring functions:• Area Under the Receiver Operating Characteristic (AUC) AUC is a widely used non-parametric evaluation measure for assessing the performance of binary classification. In our method, AUC measures the neuron's ability to distinguish between synthetic and control data pointsEQUATION• Mean Activation Difference (MAD) MAD is a parametric measure that quantifies the difference between the mean activation of the neuron on synthetic images and the mean activation on control data pointsEQUATIONwith mean control activation ā = 1 n a∈A0 a.These two chosen metrics complement each other. AUC, being non-parametric and stable to outliers, evaluates the classifier's ability to rank synthetic images higher than control images (with scores ranging from 0 to 1, where 1 represents a perfect classifier and 0.5 is random). On the other hand, MAD allows us to parametrically measure the extent to which images corresponding to explanations maximize neuron activation.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?Answer: [Yes]Justification: We provided our code and use publicly available models, datasets and methods.Guidelines:• The answer NA means that paper does not include experiments requiring code. • Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted. 
sanity checks: 
 To ensure the reliability of our proposed evaluation measure, all steps within our framework need to be subject to sanity checks [33] . In this section, we analyze the following: (1) which generative models and prompts provide the best similarity to natural images, (2) whether the model's behavior on synthetic and natural images differs for the same class, and (3) validating that COSY provides appropriate evaluation scores for true and random explanations, given a known ground truth class for the neuron.One of the key features of COSY is its reliance on generative models to translate textual explanations of neurons into the visual domain. Thus, it is essential that the generated images reliably resemble the textual concepts. In the following section, we present an experiment where we varied several parameters of the generation procedure and evaluated the visual similarity between generated images and natural ones, focusing on concepts for which we have a collection of natural images.For our analysis, we used only open-source and freely available text-to-image models, namely Stable Diffusion XL 1.0-base (SDXL) [34] and Stable Cascade (SC) [35] . We also varied the prompts for image generation. To measure the similarity between synthetic images and natural images corresponding to the same concept, we used Cosine Similarity (CS) in the CLIP embedding space with the CLIP-ViT-B/32 model [27] . We select a set of 50 random concepts from the 1,000 classes in the ImageNet validation dataset [36] . For each [concept] we use five different prompts and employ them with SDXL and SC models, generating 50 images per class. We then measure the CS between image pairs of the same class.Figure 2 illustrates the comparison across all generative models and prompts in terms of CS of generated images to natural images of the same class. The results indicate that when using Prompt 5 as input to SDXL, the synthetic images show the highest similarity to natural images. The performance is generally best with the most detailed prompt (5) and closely aligns with prompts 1, 3, and 4. Moreover, SDXL appears to be slightly more effectively realizing detailed prompts than SC. As anticipated, the poorly constructed prompt (2) results in the lowest similarity to natural images for both models. To address prompt bias and dataset dependency, we compare the object-focused ImageNet with the scene-focused Places365 (see Appendix A.3). We find that close-up prompts work well for object-centric datasets, while general prompts like "photo of" are better for scene-based datasets. If not stated otherwise, for all following experiments, Prompt 5 together with SDXL model was employed for image generation.Figure 2 : An overview of the impact of varying the prompt on the similarity between natural and synthetic images, using two text-to-image models. Left: average Cosine Similarity (CS) across all natural and synthetic images over all classes are reported. Higher CS values are better, indicating greater similarity between the images. Right: an illustration of the visual differences produced by the SDXL and SC models in response to diverse prompts for the concept "submarine", and natural images from the ImageNet validation dataset [36] . Our results show that both SDXL and SC generate similar images, with SDXL generally being more closely aligned with natural images than SC.Given the visual similarity between natural and synthetic images of the same class, we investigate whether CV models respond differently to these groups and if the activation differences indicate adversarial behavior. To this end, we employed four different models pre-trained on ImageNet: ResNet18 [37] , DenseNet161 [38] , GoogleNet [39] , and ViT-B/16 [40] . For each model, we randomly selected 50 output classes and generated 50 images per class using the class descriptions. We pass both synthetic and natural images through the models, collecting the activations of the output neuron corresponding to each class.Figure 3 (a) illustrates the distributions of the MAD between synthetic and natural images for the same class across the 50 classes. Across all models, we observe that the median activation of synthetic images is slightly higher than that of natural images of the same class. However, this difference is small, given the 0 value lies within 1 standard deviation. We also illustrate the activations of neuron 504 in the ResNet18 output layer for the "coffee mug" class in Figure 3 (b). The results indicate a strong overlap in the neural response to both synthetic and natural images. While synthetic images activate the neuron slightly more, this does not constitute an artifactual behavior or affect our framework, which we demonstrate in the following experiment. In both analyses, we observe a substantial overlap in the activations of synthetic and natural images, suggesting that the models respond similarly to both types of images. A robust evaluation metric should reliably discern between random explanations resulting in low scores and non-random explanations resulting in high scores. To assess our evaluation framework regarding this requirement, we evaluated the results of the COSY evaluation by comparing the scores of ground truth explanations with those of randomly selected explanations.Following the experimental setup in Section 4.2, we selected a set of 50 output neurons and compared the COSY scores of the ground truth explanations, given by the neuron label, with those of randomly selected explanations. The results, presented in Table 2 , consistently demonstrate high scores for true explanations and low scores for random explanations. This experiment provides further evidence supporting the correctness of the proposed evaluation procedure. An additional experiment that excludes the target class from the control dataset is presented in Appendix A.4, along with an analysis of the robustness of the evaluation measure detailed in Appendix A.6. 
evaluating explanation methods: 
 Within the scope of this section, we produce a comprehensive cross-comparison of various methods for the textual explanations of neurons. For this comparison, we employed models trained on different datasets, and we conducted our analysis on the latent layers of the models, where no ground truth is known.In this section, we evaluated three recent textual explanation methods, namely MILAN, INVERT, and CLIP-Dissect. Our analysis involves six distinct models: four pre-trained on the ImageNet dataset [36] (ResNet18 [37] , ResNet50 [37] , ViT-B/16 [40] , DINO ViT-S/8 [41] ) and two pretrained on the Places365 dataset [42] (DenseNet161 [38] , ResNet50 [37] ). The ImageNet dataset focuses on objects, whereas the Places365 dataset is designed for scene recognition. Consequently, we customized our prompts accordingly: Prompt 5 performs best for object recognition, while for scene recognition, we found that Prompt 3 is more effective. Therefore, Prompt 3 was utilized in the Places365 experiment.For generating explanations with the explanation methods, we use a subset of 50,000 images from the training dataset on which the models were trained. For evaluation with COSY, we use the corresponding validation datasets the models were pre-trained on as the control dataset. Additionally, for CLIP-Dissect, we define concept labels by combining the 20,000 most common English words with the corresponding dataset labels. For INVERT we set the compositional length of the explanation as L = 1, where L ∈ N. For more details on compute resources, refer to Appendix A.7.Results of the evaluation can be found in Table 3 . Overall, INVERT achieves the highest AUC scores across all models and datasets, except for DINO ViT-S/8 and ResNet18 applied to ImageNet, where CLIP-Dissect achieves a higher or similar score. Also across other models and datasets, CLIP-Dissect demonstrates consistently good results. Since INVERT optimizes AUC in explanation generation, it may be biased towards AUC in our evaluation, leading to higher scores. MILAN generally performs poorly, with an average AUC below 0.65 across all tasks, indicating performance close to random guessing. MILAN tends to generate highly abstract explanations, such as "white areas", "nothing" or "similar patterns". These abstract concepts are particularly challenging for a text-to-image model to generate accurately, likely contributing significantly to the low scores of MILAN. Contrary to the AUC scores, the MAD scores suggest that CLIP-Dissect outperforms INVERT for convolutional neural networks applied to both datasets. Nonetheless, in these cases, INVERT concepts also achieve consistently high scores. Otherwise, we find similar outcomes for both metrics Ψ, with MILAN achieving poor scores in all experimental settings.In addition to the general benchmarking, we aimed to study the quality of explanations for neurons in different layers of a model. Since it is well known that lower-layer neurons usually encode lower-level concepts [43] , it is interesting to see whether explanation methods can capture the concepts these neurons detect. To investigate this, we examined the quality of explanations across layers 1 to 4 and the output layer of an ImageNet pre-trained ResNet18. In addition to three prior explanation methods, we included the FALCON method in our analysis. For more details on the implementation of FALCON see Appendix A.1.4, for additional results of the original FALCON implementation see Appendix A.8, and for qualitative examples and a discussion of lower-level concepts see Appendix A.9. For each layer, we randomly selected 50 neurons for analysis.In Figure 4 we present the AUC and MAD results for all explanation methods across layers 1 to 4 and the output layer of ResNet18. While less pronounced for the AUC metric, in general, we find increasing scores for later layers across all methods and both metrics Ψ, which suggest higher concept quality in later layers. Furthermore, we find that similar to the benchmarking experiments, MILAN achieves lower scores across metrics. Both MILAN and FALCON consistently show lower performance, with AUC scores of 0.5 indicating random guessing. Nonetheless, we point out that these methods typically output semantically high-level concepts. Potentially, this is related to the inherent difficulty in describing low-level abstractions in natural language given their complexity (see Appendix A.10). In There are instances, such as neuron 260 in Figure 5 , where all explanations vary significantly. In these cases, we find that the explanation activation distributions of FALCON and MILAN often overlap with or even match the control dataset, providing the user with nearly random explanations. This observation aligns with our overall findings: both the AUC and MAD scores consistently reflect the low performance of FALCON and MILAN explanations in the COSY evaluation. Also, neurons 459 and 155 demonstrate the gap between consistently higher and lower-performing explanation methods. 
conclusion: 
 In this work, we propose the first automatic evaluation framework for textual explanations of neurons. Unlike existing ad-hoc evaluation methods, we can now quantitatively compare different neuron description methods against each other and test, whether the given explanation describes the neuron accurately, based on its activations. We can evaluate the quality of individual neuron explanations by examining how accurately they align with the generated concept data points, without requiring human involvement.Our comprehensive sanity checks demonstrate that COSY guarantees a reliable explanation evaluation.In several experiments, we show that neuron description methods are most applicable for the last layers, where high-level concepts are learned. In these layers, INVERT and CLIP-Dissect provide high-quality neuron concepts, whereas MILAN and FALCON explanations have lower quality and can present close to random concepts, which might lead to wrong conclusions about the network. Thus, the results highlight the importance of evaluation when using neuron description methods.Limitations The use of generative models involves a distinct set of limitations. For instance, text-to-image models may not include certain concepts in their training data, which can reduce their generative performance. This limitation, however, can often be addressed by analyzing the pretraining datasets and assessing model performance. Moreover, the model's capabilities of generating highly abstract concepts like "white objects" can be limited. However, the challenges with abstract concepts also reflect the descriptive quality of the provided explanations-explanations should be inherently understandable to humans. In both cases, exploring more sophisticated, specialized, or constrained models may offer improvement.Future Work Evaluation of non-local explanation methods is still a largely neglected research area, where COSY plays an important yet preliminary part. In the future, we need additional, complementary definitions of explanation quality that extend our precise definition of AUC and MAD, e.g., that involve humans to assess plausibility [44] or evaluate explanation quality via the success of a downstream task [45] . Furthermore, we plan to extend the application of our evaluation framework to additional domains including NLP and healthcare. In particular, it would be interesting to analyze the quality of more recent autointerpretable explanation methods given by highly opaque, large language models (LLMs) [46, 9] . We believe that applying COSY to healthcare datasets, where high-quality explanations are crucial, represents an impactful next step.Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?Answer: [Yes]Justification: We specify and justify the hyperparameters, models, method restrictions, type and subsets of datasets.Guidelines:• The answer NA means that the paper does not include experiments.• The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them. • The full details can be provided either with the code, in appendix, or as supplemental material. 
experiment statistical significance: 
 Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?Answer: [Yes] Justification: Our box plots and line plots include error bars.Guidelines:• The answer NA means that the paper does not include experiments.• The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper. • The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).• The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.) • The assumptions made should be given (e.g., Normally distributed errors). • It should be clear whether the error bar is the standard deviation or the standard error of the mean. • It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified. • For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates). • If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text. 
experiments compute resources: 
 Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?Answer: [Yes] Justification: In Appendix A.7 we give detailed information on the type of computer resources, inference and running time.Guidelines:• The answer NA means that the paper does not include experiments.• The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage. • The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute. • The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper). 
code of ethics: 
 Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines? 
broader impacts: 
 Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?Answer: [Yes] Justification: Our method provides more transparency of XAI methods and guides safer use.Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.• Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
safeguards: 
 Question: Does the paper describe safeguards that have been put in place for the responsible release of data or models that have a high risk for misuse (e.g., pre-trained language models, image generators, or scraped datasets)? Answer: [Yes] Justification: We mention the bias in and limitations of generative models. Guidelines: We do not provide new data or a new pre-trained model.• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort. 12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected? Answer: [Yes] Justification: All models, datasets, and methods used are cited. Guidelines:• The answer NA means that the paper does not use existing assets.• The authors should cite the original paper that produced the code package or dataset.• The authors should state which version of the asset is used and, if possible, include a URL. • The name of the license (e.g., CC-BY 4.0) should be included for each asset.• For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided. • If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset. 
 
 Appendix: Neuron description methods aim to provide insights into human-understandable concepts learned by DNNs, enabling a deeper understanding of their decision-making mechanisms. These methods provide textual descriptions for neurons in CV models. This creates a connection between the abstract representation of a concept by the neural network and a human interpretation. In general, a concept can be any abstraction, such as a color, an object, or even an idea [47] . Textual explanations of a neuron f i can originate from various spaces depending on their generation process.As defined in Section 3.1, we refer to explanation method as an operator E that maps a neuron to the textual description s = E(f i ) ∈ S, where S is a set of potential textual explanations. The specific set of explanations depends on the implementation of the particular method. We define the following subsets of textual descriptions s ∈ S:• C represents the space of individual concepts,• L represents the space of logical combinations of concepts,• N represents the space of open-ended natural language concept descriptions.These textual descriptions serve as explanations for f i generated by explanation methods.Examples for such explanation methods are MILAN [13] , FALCON [14] , CLIP-Dissect [15] , and INVERT [16] . Figure 6 shows the general principle of how E works. In Table 4 we outline the origin of textual descriptions and their corresponding set memberships for each E. Network Dissection (NetDissect) [11] is a method designed to explain individual neurons of DNNs, particularly convolutional neural networks (CNNs) within the domain of CV. This approach systematically analyzes the network's learned concepts by aligning individual neurons with given semantic concepts. To perform this analysis, annotated datasets with segmentation masks are required, where these masks label each pixel in an image with its corresponding object or attribute identity. The Broadly and Densely Labeled Dataset (Broden) [11] combines a set of densely labeled image datasets that represent both low-level concepts, such as colors, and higher-level concepts, such as objects. It provides a comprehensive set of ground truth examples for a broad range of visual concepts such as objects, scenes, object parts, textures, and materials in a variety of contexts.A concept s ∈ C ⊂ S is defined as a visual concept in NetDissect and is provided by the pixel-level annotated Broden dataset. Given a CNN and the Broden dataset as input, NetDissect explains a neuron f i by searching for the highest similarity between concept image segmentation masks and neuron activation masks. Concept image segmentation masks are provided by the Broden dataset B s (x x x) ∈ {0, 1} H×W , where a value of 1 signifies the pixel-level presence of s, and 0 denotes its absence. Neuron activation masks are obtained by thresholding the continuous neuron activations of f i into binary masks A(x x x) ∈ {0, 1} H×W . Then the similarity δ IoU between image segmentation masks and binary neuron masks can be evaluated using the Intersection over Union score (IoU) for an individual neuron within a layer:δ IoU (f i , s) = x x x∈X X X 1 (B s (x x x) ∩ A(x x x)) x x x∈X X X 1 (B s (x x x) ∪ A(x x x)).(EQUATIONThe NetDissect method is optimized to identify the concept that yields the highest IoU score between binary masks and image segmentation masks. This can be formalized as:EQUATIONNetDissect is constrained to segmentation datasets, relying on pixel-level annotated images with segmentation masks. Moreover, its labeling capabilities are confined to concepts provided within a labeled dataset. Furthermore, only individual concepts can be associated with each neuron.To overcome the limitation of explaining neurons with only a single concept, the Compositional Explanations of Neurons (CompExp) method was later introduced [12] , enabling the labeling of neurons with compositional concepts. The method obtains its explanations by merging individual concepts into logical formulas using composition operators AND, OR, and NOT. The formula length L ∈ N is defined beforehand. The initial stage of explanation generation is similar to NetDissect, a set of images is taken as input, and convolutional neuron activations are converted into binary masks. The explanations are constructed through a beam search algorithm [49] , beginning with individual concepts and gradually building them into more complex logical formulas. Throughout the beam search stages, the existing formulas in the beam are combined with new concepts. These new formulas are measured by the IoU. The maximization of the IoU score is desired to get a high explanation quality.The approach for obtaining δ IoU is the same as in Equation 5. In contrast to NetDissect, the explanations can be a combination of concepts, where s ∈ L ⊂ S. The procedure of finding the best neuron description can be formalized as:EQUATIONSimilar to NetDissect, CompExp requires datasets containing segmentation masks and is primarily applicable to convolutional neurons.MILAN [13] is a method that aims to describe neurons within a DNN through open-ended natural language descriptions. First, a dataset of fine-grained human descriptions of image regions (Milannotations) is collected. These descriptions can be defined as concepts that are open-ended natural language descriptions, where s ∈ N ⊂ S. Given a DNN and input images x x x ∈ X X X, neuron masks M (x x x) ∈ R H×W ×C are collected of highly activated image regions for f i .Two distributions are then derived: the probability p(s|M (x x x)) that a human would describe an image region with s, and the probability p(s) that a human would use the description s for any neuron. The probability p(s|M (x x x)) is approximated with the Show-Attend-Tell [48] image-to-text model trained on the Milannotations dataset. Additionally, p(s) is approximated with a two-layer LSTM language model [50] trained on the Milannotations dataset.These distributions are then utilized to find a description that has high pointwise mutual information with M (x x x). A hyperparameter λ ∈ R adjusts the significance of p(s) during the computation of pointwise mutual information (PMI) between descriptions s and M (x x x) sets, where the similarity δ WPMI is weighted PMI (WPMI). The objective for WPMI is given by: δ WPMI (s) = log p (s|M (x x x)) -λ log p(s).MILAN aims to optimize high pointwise mutual information between s and M (x x x) to find the best description for f i :EQUATIONThe requirement of collecting the curated labeled dataset, Milannotations, limits MILAN's capabilities when applied to tasks beyond this specific dataset. Additionally, another drawback is the requirement for model training.The FALCON [14] explainability method has a similar approach to MILAN. Initially, it gathers the most highly activating images corresponding to a neuron. GradCam [5] is subsequently applied to identify highlighted features in these images, which are then cropped to focus on these regions. These cropped images, along with large captioning dataset LAION-400m [51] with concepts s ∈ N ⊂ S, are input to CLIP (Contrastive Language-Image Pre-training) [27] , which computes the image-text similarity between the text embeddings of captions and the input cropped images. The top 5 captions are then extracted. Conversely, the least activating images are collected, and concepts are extracted and removed from the top-scoring concepts, ultimately yielding the explanation of the neuron.The similarity δ CLIPScore is obtained by calculating the CLIP confidence matrix, which is essentially a Cosine Similarity matrix. The aim is to find the maximum image-text similarity score between image embeddings and their closest text embeddings from a large captioning dataset:EQUATIONThis restriction significantly narrows down the range of models suitable for analysis, setting it apart considerably from other explanation methods.In its original implementation, FALCON restricts the set of "explainable neurons" based on specific parameters. These include the parameter α ∈ N, which determines the set of highly activating images for a given feature by requiring α > 10. Additionally, it employs a threshold γ ∈ R for CLIP Cosine Similarity, with a set value of γ > 0.8.These parameter settings significantly restrict the number of explainable neurons, resulting to fewer than 50 explainable neurons. This constraint prevents the necessary randomization for comparison with other methods. To address this, we set α = 0 and γ = 0. However, for the original FALCON implementation, we retain the original settings of α and γ and calculate Ψ across all "explainable neurons". In our experiments on ResNet18, FALCON can only be applied to layers 2 to 4.CLIP-Dissect [52] is an explanation method that describes neurons in vision DNNs with open-ended concepts, eliminating the need for labeled data or human examples. This method integrates CLIP [27] , which efficiently learns deep visual representations from natural language supervision. It utilizes both the image encoder and text encoder components of a CLIP model to compute the text embedding for each concept s ∈ C ⊂ S from a concept dataset and the image embeddings for the probing images in the dataset, subsequently calculating a concept-activation matrix.The activations of a target neuron f i are then computed across all images in the probing dataset X X X. However, as this process is designed for scalar neurons, these activations are summarized by a function that calculates the mean of the activation map over spatial dimensions. The concept corresponding to the target neuron is determined by identifying the most similar concept s based on its activation vector. The most highly activated images are denoted as X X X s ⊂ X X X.SoftWPMI is a generalization of WPMI where the probability p (x x x ∈ X X X s ) denotes the chance an image x x x belongs to the example set X X X s . Standard WPMI corresponds to cases where p(x x x ∈ X X X s ) is either 0 or 1 for all x x x ∈ X X X, while SoftWPMI relaxes this binary setting to real values between 0 and 1. The function can be formalized as:EQUATIONThe similarity function δ SoftWPMI aims to identify the highest pointwise mutual information between the most highly activated images X X X s and a concept s. This optimization search is expressed as:EQUATION)A drawback of CLIP-Dissect lies in its interpretability; descriptions are generated by the CLIP model, which itself is challenging to interpret.Labeling Neural Representations with Inverse Recognition (INVERT) [16] shares the capability of constructing complex explanations like CompExp [12] but with the added advantage of not relying on segmentation masks and only needing labeled data. The method obtains its explanations by merging individual concepts into logical formulas using composition operators AND, OR, and NOT. It also exhibits greater versatility in handling various neuron types and is computationally less demanding compared to previous methods such as NetDissect [11] and CompExp [12] . Additionally, INVERT introduces a transparent metric for assessing the alignment between representations and their associated explanations. The non-parametric Area Under the Receiver Operating Characteristic (AUC) measure evaluates the relationship between representations and concepts based on the representation's ability to distinguish the presence from the absence of a concept, with statistical significance. The probing dataset with the concept present is labeled as X X X 1 , while the dataset without the concept is labeled as X X X 0 .The goal of INVERT is to identify the concept s ∈ L ⊂ S that maximizes δ AUC with the neuron f i .Here, s can be a combination of concepts. The optimization process resembles that of CompExp, employing beam search [49] to find the optimal compositional concept. The top-performing concepts are iteratively selected until the predefined compositional length L ∈ N is reached.The similarity measure δ AUC is defined as:EQUATION)The objective of INVERT is to maximize the similarity δ AUC between a concept s and the neuron f i , which can be described as:EQUATION)INVERT is constrained by the requirement of a labeled dataset and is computationally more expensive compared to CLIP-Dissect.In the example shown in Figure 1 , we used the default settings of the explanation methods to generate explanations for neuron 80 in the avgpool layer of ResNet18. For CLIP-Dissect, we used the 20,000 most common English words as the concept dataset and the ImageNet validation dataset [36] as the probing dataset. We employed Stable Diffusion XL 1.0-base (SDXL) [34] as the text-to-image model, using the prompt "realistic photo of a close up of [concept]" to generate concept images, with[concept] being replaced by the textual explanation from the methods. We generated 50 images per concept for 50 randomly chosen neurons from the avgpool layer of ResNet18. For evaluation, we also used the ImageNet validation dataset as the control dataset.To address prompt bias and dataset dependency, we extended our analysis by comparing results on the object-focused ImageNet with the scene-focused Places365 dataset. Our results show a significant difference based on prompt selection and dataset: close-ups work well for object-centric datasets like ImageNet, while more general prompts, such as "photo of [concept]", are more suitable for scene-based datasets like Places356 (see Figure 7 ). Additionally, we evaluated model performance using different similarity metrics across both datasets. Alongside Cosine Similarity (CS), we introduced two more distance measures: Learned Perceptual Image Patch Similarity (LPIPS) [53] , which calculates perceptual similarity between two images and aligns well with human perception, using deep embeddings from a VGG model. Additionally, we included "Euclidean Distance" (ED) to capture the absolute differences in pixel values. The results of these evaluations are presented in Table 5 .In addition to the results presented in Table 2 , we conducted the same experiment with the ground truth images excluded from the control dataset. The corresponding results, shown in Table 6 , closely align with those obtained when the ground truth class was included. The findings are largely consistent with those obtained when the ground truth class was included.In addition to comparing natural and synthetic images as in Section 4. aspects of similarity and complement each other. We compute the average CS, ED, and LPIPS for each class and determine the overall class average. Table 7 provides a detailed overview of the results quantifying the similarity within synthetic images using CS and ED. When evaluating these results, it is important to note that high scores do not necessarily indicate optimal outcomes, as they suggest nearly identical images, which may lack intraclass distance. Conversely, very low scores imply significant differences among images, which might not capture the essence of the concept adequately. Ideally, we aim for somewhat similar yet slightly varied images representing the same class. The results show that the Stable Cascade (SC) model consistently achieves higher scores across all prompts compared to the Stable Diffusion XL 1.0-base (SDXL) model. Notably, it obtains the highest score for the two most elaborate prompts (4, 5) . This indicates that the SC model tends to offer less intraclass distance in visually representing concepts. In this experiment, our goal is to evaluate the stability of the image generation method employed, aiming to ensure consistent results within our COSY framework. We achieve this by varying the seed of the image generator and observing the impact on image generation. We anticipate consistent image representations across different model initializations, thus ensuring the stability of our framework.For our analysis, we utilize ResNet18 and focus on its output neurons, as the ground-truth labels associated with these neurons are known. We randomly select six classes s from the ImageNet validation dataset [36] and examine the corresponding f i class output neurons using COSY. Here, we exclude the s class from A 0 and let A 1 represent the s class. To ensure robustness, we initialize the text-to-image model across a random set of 10 seeds. Our analysis involves calculating the first (mean) and second moment (STD) using Ψ AUC , as well as evaluating the intraclass image similarity (refer to Section A.5) within each synthetic ground truth class.The results for our experiment, as shown in Table 8 , demonstrate remarkably high AUC scores, indicating near-perfect detection of synthetic ground truth classes across all image model initializations. Furthermore, the standard deviation is exceptionally low, suggesting consistent image generation regardless of the chosen seed. The intraclass similarity values indicate a certain degree of distance in the generated images, indicating high similarity yet distinctiveness. This intraclass distance is desirable, ensuring that the images are not identical but share common characteristics.These findings underscore the reliability and consistency of our image generation pipeline within our COSY framework. The high stability of text-to-image generation across different seeds and the diversity of image similarity contribute to the robustness of our approach.For running the task of image generation for COSY we use distributed inference across multiple GPUs with PyTorch Distributed, enabling image generation with multiple prompts in parallel. We run our script on three Tesla V100S-PCIE-32GB GPUs in an internal cluster. Generating 50 images for 3 prompts in parallel takes approximately 12 minutes. Given that the original implementation of FALCON only provides results for their defined "explainable neurons" (see Appendix A.1.4), we included additional results comparing all methods based on this subset of neurons. Specifically, there are 7 explainable neurons in layer 2, 5 in layer 3, and 15 in layer 4. Figure 8 presents these results. A.9 Qualitative Examples of Lower-Level ConceptsThe performance of generative models may present challenges when dealing with abstract concepts.To assess this concern and provide a basis for further discussion, we have included an additional example of lower-layer concepts in Figure 9 .While COSY focuses on measuring the explanation quality, another open question is how broad or abstract are the concepts provided as textual explanations. This question of how specific or general an individual neuron is described by the explanation, might be relevant to different interpretability applications. For example, research fields where the user aims to deploy the same network for multiple tasks with varying image domains. In this case, describing a neuron's more general concept such as "a round object" might be more informative than a more (domain-)specific concept such as "a tennis ball" for the network assessment. To provide insight into the broadness of concepts, we assessed whether the similarity between images generated based on the same concept changes from more general to more specific concepts. From the illustrated Feature Visualizations, we can observe that these neurons detect low-level abstractions. However, the methods studied generally fail to provide low-level explanations, instead attributing more complex explanations.In our experiment, we define the broadness of a concept based on the number of hypernyms in the WordNet hierarchy [54] . The more specific a concept the larger the number of hypernyms. We choose two ImageNet classes ("ladybug", "pug") and generate 50 images for each concept as well as each hypernym of both concepts (with the most general concept being "entity"). Then, we measure the Cosine Similarity (CS) of all images generated based on the same concept. The box plot of the CS across both concepts and all hypernyms, in Figure 10 indicates that we do not find a correlation. Thus, we hypothesize that the chosen temperature of the diffusion model has a stronger effect on image similarity than the broadness of the prompt used for image generation. Justification: We state our contributions at the end of in Section 1 and refer to the sections where each contribution is demonstrated.Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.Justification: We mention biases in models and methods we use.Guidelines:• The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.• If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics. • The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).https://github.com/lkopf/cosy 38th Conference on Neural Information Processing Systems (NeurIPS2024). 


Decision: This paper proposes a framework to evaluate the text explanations of neurons, Concept Synthesis (CoSy). Given textual explanations, CoSy uses a generative model conditioned on textual input to create data points representing the textual explanation. The neuron’s response towards these data points are compared with the response to control data points to provide a quality estimate of the given explanation. This is the first general-purpose, quantitative evaluation framework that enables the evaluation of explanations for CV models. With extensive meta-evaluation experiments, this work analyze the choice of generative models, prompts, and explanation methods.

The reviewers agree on the positive ratings of this paper, and the rebuttals addressed most concerns. That said, the reviewers mentioned some aspects of improvement, e.g., about the empirical rigor of the evaluation procedure and the coverage of the concepts. There can be further inquiry into the mechanisms (and further, the developments) of the models. But this paper itself already marks a solid, significant and valuable contribution.

Reviews: review: paper_summary: paper_summary: This paper proposes an automatic evaluation for textual explanations of neurons in vision models. The evaluation works by using a text-to-image model to generate images based on the explanation of a neuron. Then, these images are passed through the vision model and that neuron’s activations are recorded. These activations are compared to neuron activations on control images that should have nothing to do with the explanation. A good explanation should yield generated images that produce high neuron activations, as compared to control images. Two metrics are used to measure the difference in neuron activations. The paper validates the evaluation framework by checking that (1) generated images are similar to natural images of class concepts, (2) vision model neuron activations are similar for generated and natural images, and (3) that “ground-truth” explanations (class concept labels) receive high scores while “random” explanations (random class labels) receive low scores. Finally, the authors apply their evaluation to several prominent neuron explanation methods and draw some conclusions about which methods work better and at which layers.
strength_weakness: strength_weakness: - Very important: The meta-evaluation in this paper suffers a bit on two fronts. First, it is difficult to say it is a meta-evaluation in the sense that we could plug another evaluation procedure into this framework and mesaure how different evaluation results correlate with some *third,* more ground-truth or utility-driven evaluation of explanation methods. Take for example the meta-evaluation of automatic machine-translation metrics. Metrics like BLEU and ROUGE are accepted on the basis of their correlation (or lack thereof) with human judgment of translation quality. What would make the meta-evaluation in this paper more of a meta-evaluation is if it compared with, for instance, evaluations from prior work described in “Prior Methods for Evaluation”, using some third ground-truth/utility-oriented evaluation as a target measure (see e.g. evaluations in https://arxiv.org/pdf/2211.10154 as possible targets). To clarify why I don’t think the results in Sec. 4.3 count as such a ground-truth evaluation, my second point here is that there is a major distribution shift in what we want to measure between typical uses of textual neuron explanations and the validations conducted in Sec. 4. Specifically, Sec. 4 focuses exclusively on evaluations of “output neurons”, i.e. class logits, whereas textual neuron explainers are applied almost exclusively to intermediate hidden neurons in models. This means that even a proper meta-evaluation comparing CoSY against competing explanation methods would be limited by a narrow focus on output neurons and not intermediate neurons. I wonder, could the paper inclue a meta-evaluation experiment utilizing a model that has been trained with intermediate representation supervision, like a kind of concept-bottleneck model, so there could be some ground-truth label for an intermediate neuron (at least as ground-truth as using output neurons, which are learned with direct supervision)?
- Important: I have some other doubts about how the evaluation will work for neurons that represent low-level (perhaps early layer neurons) or abstract features (possibly middle/later layer neurons). For instance, how about low-level edge/shape features, or abstract features like “white objects”. It seems guaranteed that the control images will share many low-level features with the generated images. And it seems very possible that the control images include white objects. These cases seem problematic in that they could lead to low AUC or MAD scores even if the explanation is correct. Can the control images be generated in a conditional way that encourages them to lack the concepts in the proposed explanations?
- Important: Concepts could overlap or be nested in semantic hierarchies, and I am not sure that the evaluation framework could clearly discriminate between good and bad explanations in these settings. For example, we might want to distinguish between “red apples”, “red fruits”, and “red objects” as explanations for a neuron. In order to do this, we need the generated images for each explanation to achieve a sufficient amount of diversity. It would be a problem if when the text-to-image model gets the phrase “red fruits”, it generates mostly red apples (a very salient example of red fruit). So, I would suggest that one meta-evaluation experiment focus on measuring diversity or coverage of the generated images.
questions: questions: - There has been a shift in the literature from treating neurons as the unit of analysis to feature directions, starting with TCAV (https://arxiv.org/abs/1711.11279) and extending through work like CRAFT (https://arxiv.org/pdf/2211.10154). One example of automatic textual explanation of feature directions is given for LLMs in Sec. 3 of https://arxiv.org/pdf/2309.08600 (as well as https://transformer-circuits.pub/2023/monosemantic-features#global-analysis-interp-auto-acts). I don’t know if any existing methods for vision models could be adapted to focus on latent feature directions rather than neurons, but this would be a ripe direction for future methods.
- Based on the above, the related work section might also point to (1) https://arxiv.org/pdf/2309.08600, (2) https://transformer-circuits.pub/2023/monosemantic-features#global-analysis-interp-auto-acts, and (3) https://arxiv.org/pdf/2211.10154, and possibly point to automatic, model-based evaluations of local explanations like https://arxiv.org/pdf/2312.12747.
- I’m curious what you would think of fitting a logistic classifier to the neuron activations and computing that classifier’s AUC. How would that differ from your non-parametric AUC?
limitations: limitations: I think the limitations discussion is sufficient.
paper_summary: paper_summary: The paper presents a novel, architecture-agnostic framework called COSY for quantitatively evaluating textual explanations of neurons in deep neural networks. The framework utilizes generative models to create synthetic images based on textual explanations, allowing for a standardized comparison of neuron responses to these synthetic images versus control images. Through a series of meta-evaluation experiments, the authors demonstrate the reliability and practical value of COSY by benchmarking various concept-based textual explanation methods for computer vision tasks, revealing significant variability in their quality.
strength_weakness: strength_weakness: 1. The effectiveness of the COSY framework relies heavily on the quality of the generative models used to create synthetic images. If the generative models are not trained on a diverse set of concepts, they may fail to produce accurate synthetic images, thereby affecting the evaluation's reliability.

2. As mentioned by the authors, the INVERT method optimizes for the AUC metric during explanation generation; it may be biased towards achieving higher AUC scores in the COSY evaluation, potentially leading to an overestimation of its performance compared to other methods. 

3. As noted in the paper, the COSY framework demonstrates a decline in explanation quality for neurons in the lower layers of the network, which typically encode lower-level features. Could the authors provide more insight into this phenomenon? Is it possible that this is due to limitations in the generative models' capabilities, given that lower layers generally encode more basic concepts which might not be well-represented by the generative model?
questions: questions: see the Weaknesses part
limitations: limitations: Yes, the authors adequately addressed the limitations.
paper_summary: paper_summary: The authors present a new framework designed to evaluate the quality of textual explanations for neurons in deep neural networks (DNN). To this end, the paper introduces CoSY, which aims to provide a quantitative, architecture-agnostic evaluation method for these explanations, addressing the challenge of the lack of unified, general-purpose evaluation metrics in the field. Given a neuron $f$ of a DNN and explanation $s \in S$, CoSY evaluates the alignment between the explanation and a neuron in three simple steps. It utilizes a generative model to create synthetic data points from textual explanations and compares the neuron's response to these data points with its response to control data points. This comparison provides a quality estimate of the textual explanations. Further, they perform a large set of empirical analyses to quantify the effectiveness of their proposed AUC and MAD metrics.
strength_weakness: strength_weakness: 1. The authors indicate that "the results using prompt 5 as input to SDXL leads to the highest similarity to natural images" --- isn't there an implicit bias towards images with a single object in these results? Given that, most of the images in datasets like ImageNet consist of single objects, generating synthetic images using a concept and comparing them with natural images should only work for clean natural images, where the concept is explicitly observable.

2. The authors argue that "different methods devised their evaluation criteria, making it difficult to perform general-purpose, comprehensive cross-comparisons." While it is true that the community should work towards common benchmark metrics, it would be great if the authors could clarify what is the problem with the existing quantitative metrics. Further, why can't someone benchmark existing metrics?

3. In Lines 159-161, the authors mention that they employ cosine similarity (CS) to measure the similarity between synthetic images and natural images corresponding to the same concept, but they never discuss the problems or motivations of using CS. For instance, cosine similarity is not a well-calibrated metric (also observed in Fig 2 where we observe very little variance in cosine values across different prompts) for models like CLIP, where the representations are densely packed using a large number of concepts.

4. Can the authors comment how possible techniques to alleviate the dependency of a text-to-image generative model in their evaluation pipeline?
questions: questions: 1. Should we expect the AUC and MAD evaluation metrics to correlate? i.e., will an explanation method achieving high AUC also obtain a high MAD score? If yes, why don't we observe this consistently in the empirical analysis?

2. Is quantifying the synthetic image reliability using only 10 random concepts from the 1,000 classes of ImageNet in Figure 2 justified? Do these results hold for a larger number of concepts?

3. Is the aggregation of the activation values done using absolute sum or signed sum of the activations? This is important as negative activations may bias the aggregate value and impact the AUC and MAD metrics.

4. The authors present a very interesting analysis in Fig 4, where they aim to study the quality of explanations for neurons in different layers of a model. However, the error in the values in Fig. 4 is very large, questioning the conclusions from the figure. Moreover, given that the first few layers of a model detect generic low-level features like corners, edges, angles, and colors, why don't we observe near-perfect cosine similarity?
limitations: limitations: Please refer to the weakness section for more details.
paper_summary: paper_summary: The authors propose a framework for evaluating Neuron Annotation methods which label a neuron in a given vision model, via a textual description. This framework is based on generating a set of images using a text2image model, given the predicted textual description of the neuron which acts as the textual input prompt to the generative model. The idea is that if the textual description truly describes the neuron, then the generated image should activate that neuron more than that of a random baseline image from the training dataset the model was trained on (denoted as the control image). The authors use two scoring functions: the AUC and MAD for quantitative evaluation. The authors further evaluate the effectiveness of the evaluation framework, showing that it is a valid way. They then proceed to evaluating existing Neuron Annotation works, and drawing findings and conclusions, such as the fact that some methods are generating random textual descriptions of neurons.
strength_weakness: strength_weakness: I could not really find major weaknesses that are grounds for rejection. There are some moderate weaknesses:

- [W1] How does the proposed evaluation framework relate to the other evaluation methods used in existing Neuron Annotation works? Do they align well with the new proposed measure? For example, does the proposed evaluation framework align well with the BERTScores for the human annotations of MILAN? In CLIP-Dissect, they labeled the neurons of the final classification layer and measured accuracy with the ground-truth class names. Does the proposed evaluation framework align well here also? 
- [W2] The pool of models is a bit small. More models (especially for for ImageNet) should be analysed. ImageNet ResNet50 is a common model in most Neuron Annotation works and should be reported. What about self-supervised models, such as DINO? How do they compare to classifiers? MILAN also performs analysis on these models. 
- [W3] There is another related Neuron Annotation work [R1], which is only applicable to ViTs. Here, attention heads and tokens are considered as Neurons and labeled. The authors may extend the analysis to this work to strengthen the paper, given that [R1] is a mathematically-valid decomposition of the ViT, and therefore should align best with the proposed evaluation framework compared to other Neuron Annotation works using the ViT.
- [W4] The Area Under the Receiver Operating Characteristic (AUC) in section 3.2 seems to be a measure which does not involve generating a curve. But usually AUC means that we have a curve in the first place before taking the area under it. What is the curve in the author's case?

Other minor weaknesses: 
- The text2image models are very time-consuming (the authors also mention that it takes 12 min using a parallel GPU setup, for generating 50 images). This limits the applicability to new users that want to evaluate their works. It would be nice to report the speed of different Stable Diffusion models along in Section 4.1, and include other fast-optimized and less-computationally expensive models -- i am not an expert in the text2image field so i do not know which models in the current literature are faster than the ones the authors use. Fortunately, given how fast the generative text2image field is moving, this problem will not be a problem in the very near future. 
- The fact that CLIP-dissect achieves good results in not surprising - because CLIP-Dissect interprets CLIP Neurons rather than CNN/ViT neurons. Their method is based on scaling CNN/ViT neuron activations using CLIP. Therefore, any image-text pair judged low by CLIP will also reduce the CNN/ViT neuron activation value for the corresponding image. In essence, one could think of CLIP-dissect as an amplified value of the CLIP image-text score (amplified by the CNN/ViT neuron activation value for the same image). And since CLIP itself is a very strong model and especially in scoring image-text inputs, it is not surprising that is performs best. 
- Line 156, "synthetic" should be replaced by "natural images" (as you already used "generated images" in line 155). Also, please be consistent with terminology. 

[R1] INTERPRETING CLIP’S IMAGE REPRESENTATION VIA TEXT-BASED DECOMPOSITION, ICLR 2024
questions: questions: In general, i feel this paper is a clear accept to me, and it will have a good impact in the field. I would like that the moderate weaknesses are addressed. While I understand that Rebutal time is limited, and computation time of the proposed framework is also time-consuming, I encourage the authors to address as much as they can from the moderate weaknesses (W1-W2-W3-W4).  

The minor weaknesses are just comments that can be included in the final manuscript.
limitations: limitations: Limitations are discussed. No special issues with negative societal impact.

Comments: title: Response to rebuttal
Response to rebuttal
comment: Thank you for your positive feedback and support. We agree that the additional experiments strengthen the paper, and we are already conducting more, which we will include in the revised manuscript along with the reviewers' comments.

Regarding [A6], we understand now that your comment was about the inherent bias of the CLIP-Dissect method towards explaining CLIP models, rather than towards the predefined concepts. Thank you for the clarification.

Thank you again for your insights and encouragement!
We appreciate your detailed comment and apologize for the confusion regarding our references in [A3]. You are correct; we intended to refer to Appendix A.6 and Figure 8 in the original manuscript.

Thank you for clarifying the approach of fitting a logistic classifier to the neuron activations. Comparing AUROC derived from logistic regression with the non-parametric AUC is an interesting idea. We appreciate your suggestion and will consider incorporating this approach in our future analyses.

Thank you again for your thoughtful feedback and for maintaining your score.
Thanks for the thorough response! Some comments below:

>Choice of wording:…Sanity Checks

Thanks! Sanity Checks sounds good to me.

>Comparison to prior evaluation:

Thanks, I’m pretty sympathetic to the point that actually comparing against other evals fairly is difficult here. It seems acceptable, though not totally ideal, to judge how reasonable each evaluation framework seems on the merits. 

>Concept-bottleneck for lower-level representations: ...We believe the ground-truth nature of CB concepts and last-layer labels to be often similar. Specifically, mostly during training, intermediate concepts are learned in a supervised manner, often using a similar loss function as the output of the model [1]. Therefore we consider that there is little difference between CB neurons and output neurons.

Well, a concept could be “fur” or “leg”. I think these concepts are much more low-level than the classes in ImageNet, which includes many specific animals.

I agree that the loss functions may be similar. 

Overall, I think there is still some important distribution shift between intermediate (CB) concepts and class/label concepts. 

> [A2] Similarity of generated concepts to control data: 

Ok I agree with the claim about “white objects”. The control data should have few white objects, while the hypothesis data could have many, and MAD should reflect this.

But I am still concerned about cases with other low level features, like edge detectors. I agree that with the claim about “inherent difficulty in describing low-level abstractions in natural language due to their complexity.“ It may be hard to describe an edge detector in words. But it would be nice to have evals that reflect this and penalize methods that fail to do this, if that is what a feature truly represents! The concern right now is that both the generated and control images will have a bunch of edges in them. So I think an even better eval would try to *remove* the the hypothesis feature from the control images. 

>[A3] Diversity of generated images or concept broadness…Appendix A.8 and Figure 9

I’m not sure I see A.8 and Fig 9. I see A.6 and Fig 8? Either way, based on A.6 and the rebuttal, I can accept that the generated images seem diverse enough for purposes of rank ordering explanations by their quality. It could be nice to show that the rank ordering of methods is similar across different diffusion model sampling temperatures (as well as other details of the diffusion model, of course), but I’m satisified with the results here.

>Thus we considered only the canonical basis, to establish a fair comparison.

Makes sense!

>[Q3] Fitting a logistic classifier to the neuron activations:

Right, so you could take the activations for the control images and activations for the generated images and try to classify them as control vs. generated with a logistic regression. So it’s a regression with one feature, which is the neuron activation. Then you can compute an AUROC for this logistic model. 

Contrast this with the AUC derived from a pairwise comparison of all possible (a,b) activation pairs across the two groups, which is non-parametric. 

I actually don’t know what the difference would be. Just an idea.

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Based on the above discussion, I continue to be happy with the paper and maintain my score of 7.
I thank the authors for the rebuttal and the extra experiments they have conducted. I am generally happy and positive about this paper, and I will retain my score as an "Accept". The authors should, however, incorporate these extra experiments, as well as other reviewers comments, in the revised manuscript. 

Regarding [A6], i was referring to the bias that CLIP-Dissect method carries towards CLIP (it explains CLIP rather than other models such as ResNet-50 due to the reasons I mentioned). I was not referring to the authors's method bias. This was not really a weakness, rather a justification of a finding of why CLIP-Dissect performs better.

I wish the authors the best of luck and congratulations!
Thank you for taking the time to review our rebuttal and for your feedback. We're glad we could address your concerns and sincerely appreciate your revised assessment.
Thank you for your detailed rebuttal response. Your responses have clarified most of my concerns. I increase my score to "Weak Accept".