Narrowing Information Bottleneck Theory for Multimodal Image-Text Representations Interpretability
Authors: Zhiyu Zhu
Zhibo Jin
Jiayu Zhang
NAN YANG
Jiahao Huang
Jianlong Zhou
Fang Chen
Venue: ICLR 2025 Poster (2025)
Keywords: Interpretability
CLIP
Primary area: interpretability and explainable AI

Abstract: The task of identifying multimodal image-text representations has garnered increasing attention, particularly with models such as CLIP (Contrastive Language-Image Pretraining), which demonstrate exceptional performance in learning complex associations between images and text. Despite these advancements, ensuring the interpretability of such models is paramount for their safe deployment in real-world applications, such as healthcare. While numerous interpretability methods have been developed for unimodal tasks, these approaches often fail to transfer effectively to multimodal contexts due to inherent differences in the representation structures. Bottleneck methods, well-established in information theory, have been applied to enhance CLIP's interpretability. However, they are often hindered by strong assumptions or intrinsic randomness. To overcome these challenges, we propose the Narrowing Information Bottleneck Theory, a novel framework that fundamentally redefines the traditional bottleneck approach. This theory is specifically designed to satisfy contemporary attribution axioms, providing a more robust and reliable solution for improving the interpretability of multimodal models. In our experiments, compared to state-of-the-art methods, our approach enhances image interpretability by an average of 9\%, text interpretability by an average of 58.83\%, and accelerates processing speed by 63.95\%. Our code is publicly accessible at https://github.com/LMBTough/NIB.

Published as a conference paper at ICLR 2025
NARROWING INFORMATION BOTTLENECK THEORY FOR MULTIMODAL IMAGE-TEXT REPRESENTATIONS INTERPRETABILITY
Zhiyu Zhu', Zhibo i Zhang", Nan Yang', Jiahao Huang", Jianlong Zhou' & Fang Chen University of Technology Sydney', SuZhou Yierqi, University of Sydneyy
ABSTRACT
The task of identifying multimodal image-text representations has garnered in- creasing attention, particularly with models such as CLIP (Contrastive Language- Image Pretraining), which demonstrate exceptional performance in learning com- plex associations between images and text. Despite these advancements, ensuring the interpretabilityy of such models is paramount for their safe deployment in real- world applications, such as healthcare. While numerous interpretability methods have been developed for unimodal tasks, these approaches often fail to transfer ef- fectively to multimodal contexts due to inherent differences in the representation structures. Bottleneck methods, well-established in information theory. have been applied to enhance CLIP's s interpretability. However, they are often hindered by strong assumptions intrinsic randomness. To overcome these challenges, we propose the Narrowing Information Bottleneck Theory, a novel framework that fundamentally redefines the traditional bottleneck approach. This theory is specif- ically designed to satisfy contemporary attribution axioms, providing a more ro- bust and reliable solution for improving the interpretability of multimodal models. In our experiments, compared to sate-offtthearr methods, our approach enhances image interpretability by an average of 9%, text interpretability by an average of 58.83%, and accelerates processing speed by 63.95%. Our code is publicly acces- sible at /t/ttts::/gittee
com Beto://MToughhhht
INTRODUCTION
CLIP (Contrastive Language-Image Pretraining) has rapidly become a pivotal model in the field of multimodal learning, especially excelling in its ability to connect the visual and textual modali- ties (Lin al. 2023p. By training on large-scale image-text pairs collected from the internet, CLIP is capable of performing zero-shot classification and image-text retrieval tasks, making it an indis- pensable component of modern generative artificial intelligence (Novack al. 2023 Jiang & Ye, 2023). With its strong visual-textual understanding capabilities, CL.P can generate, classify, and explain content without the need for fine-tuning, providing robust support for various generative Al applications. As one of the most representative Multimodal Image-Texx Text Representation (MITR) methods, CLIP's core strength lies in mapping images and texts into a shared embedding space, significantly enhancing the performance of multimodal tasks.
Despite its outstanding performance in MITR tasks, developing effective interpretability methods to reveal CLIP's dicision-making mechanisms has become increasingly important. The black-box na- ture of CLIP's multimodal embeddings presents significant challenges in high-risk applications such as medical diagnosis and content moderation, where transparency and reliability are erucial (Eslami ett al. [202 Tong et al., (2024) Yuan et al. 2024 Zhu et al., 2024b). A deeper understanding of how CLIP establishes associations between visual and textual representations essential to ensure the transparency and trustworthiness of its outputs.
There have been numerous interpretability methods focused on unimodal tasks Ribeiro et al. (201)); Sundararajan et al. (2017); Zhu et al. (2024a). but these methods are not designed for the unique
*Corresoonding author; fang.chen uus..du..a..

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characteristics of MITR tasks, resulting in suboptimal performance when directly applied to such tasks. However, there are existing interpretability methods specifically developed for MITR tasks. Despite their development, these methods often suffer from randomness issues, requiring additional sampling or loss information (Wang et al. 2023), which leads to a crisis of trust in the interpretability method itself. These issues will be further analyzed in the related work section.
Given that CLIP can generate unique image and text representations without the need for additional samples and can directly establish their correlations, it is possible to design an interpretability al- gorithm that unveils the mechanisms behind these correlations without requiring extra sampling. M2IB (Wang al., 2023). based on the Information Bottleneck Principle (IBP), proposes an inter- pretability method that does not require additional samples. This method controls the amount of feature information through a Bottleneck layer and optimizes the parameters of this layer to maxi- mize the mutual information between the representations and the task target while minimizing the correlation between the representations and the original sample. Although IBP has solid theo- retical foundation in information theory, in practice, its reliance hyperparameters and random sampling often introduces bias into the interpretation results. We will discuss this issue detail in Section 3.2
To address the aforementioned challenges, we propose a novel Narrowing Information Bottleneck Theory (NIBT). Through rigorous theoretical derivation, NIBT effectively eliminates the random- ness and hyperparameter dependency in IBP, resulting in more deterministic interpretability out- comes. Additionally, we introduce a concept of negative property, which identifies feature dimensions that negatively impact the model's predictions, further enhancing the model's inter- pretability, Our Contributions as follows:
We systematically summarize existing MITR interpretability methods and highlight the limitations.
We propose and derive the novel Narrowing Information Bottleneck Theory, which enables interpretation of MITR tasks without randomness, while preserving the advantages of the IBP.
Our research significantly improves the interpretability of the CLIP model, and we release our method as open-source for further research and application.
RELATED WORK
2.1 CONTRASTIVE LANGUAGE- SANGGE PRETRAINING (CLIP)
Radford et al. (2021) introduced CLIP, which learns multimodal embeddings of images and text by training image and text encoders on large-scale image-text paired data. This enables CLIP to estab- lish connections between the two modalities within a unified embedding space, facilitating zero-shot transfer, where the model can make predictions based on natural language descriptions without re- lying on task-specific labeled data. However, the complexity of these multimodal tasks necessitates focus on interpretability to ensure that the model's decisions are grounded in meaningful features. Studying the interpretability of CLIP helps verify whether the model genuinely understands the relationship between vision and language, opposed to relying spurious correlations in the data.
2.2 TRADITIONAL INTERPRETABILITY METHODS
Traditional interpretability methods for deep learning models were initially designed for unimodal tasks. Early methods. such as Saliency Maps, generate fine- iinegrrined heatmaps by computing the gradient of the model's output with respect to input pixels. However, these methods are sensitive to noise and often yield coarse explanations. Grad-CAM (Selvaraju et al. (017), by computing the gradient activation maps in convolutional layers, produces class-specific heatmaps, making the explanations more intuitive, particularly for convolutional neural networks (CNNs). RISE (Pet- siuk et al. 2018) further advances the field by introducing a black-box method that applies random masks to different regions of the input image, observes changes in the model's 's output, and gener- ates heatmaps that account for both global and local interpretability. RISE's advantage lies in its model-agnostic nature, making it applicable to any architecture. However, due its reliance on
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random sampling, it is computationally expensive and may introduce some noise. LIME Ribeiro et al.] (2016), another black-box method, perturbs the input locally and trains a surrogate model to provide locally linear explanations, making applicable any model, though it can sometimes pro- duce inaccurate explanations in complex tasks. Overall, these interpretability methods designed for unimodal tasks do not perform well in multimodal tasks,
demonstrate in our experiments.
With the introduction of the Sensitivity Axiom and Implementation Invariance Axiom by Sundarara- jan al. (2017), point-wise interpretable methods have rapidly evolved. The Sensitivity Axiom requires the sensitivity of a model's output to align with its attribution values, while the Imple- mentation Invariance Axiom demands that functionally equivalent models yield the same attribution results, regardless of implementation. Currently. the most ad advanced attribution methods based on adversarial attacks Jin et al. (2024) such as AGl (Pan et al. 2021) and MFABA (Zhu et al. 2024c), satisfy both axioms and have shown strong interpretability for traditional CNN models. However, these methods have not been optimized for multimodal tasks, and directly modifying their loss func- tions for multimodal tasks is infeasible. Moreover, they primarily designed for unimodal tasks, rely on downstream tasks for explanations, and lack adaptation to multimodal contexts. As a result, while traditional interpretability methods have made progress in unimodal tasks, there remains significant gap in addressing multimodal tasks and novel models like CLIP, requiring further opti- mization and extension.
2.3 INTERPRETABILITT METHODS FOR MULTIMODAL TASKS
Currently, existing interpretability methods for multimodal tasks still exhibit several limitations that require improvement, as shown in Table [ In the following sections, we will provide a detailed explanation of the causes and effects of these limitations.
No Extra Example indicates that no additional samples are required during the interpretation pro- cess, which is crucial because in real-world scenarios, do not know what samples to select, can we explain why a particular pair of samples are correlated. For instance, if we aim to explain which parts of image depict a cat, the image in the CLIP model already exhibits high activation with respect to the text cat. Therefore, we should not need to reference 100 additional images of cats and 100 images without cats. A well-trained model that already understands the semantics should not require such sampling. No Randomness means that the calculation process involves no ran- domness, as randomness reduces trust the interpretability method. No Specific Structure means that the method does not depend on particular model structure. No Info Loss ensures that no in- formation is lost during the interpretation process, such as interpreting only subset of the model's output. Current Model indicates that the method explains the model as it currently exists, without constructing a new model for interpretation. No Downstream Task means that no downstream tasks required for the explanation process.
Comparison of interpretability methods based several criteria: whether they require no extra examples, no randomness, need no specific structure, avoid information loss, explain the current model, and don't 't rely on downstream tasks.
Table 1:
Method
Extra Example
Randomness
No Specific Structure
Current Downstream Task
COCOA
O
TEXTSPAN
0
Hossain et al.
0
LICO
0
FALCON
0
M21B
NIB (Ouss)
M2IB (Wang et al. 2023) introduced a multimodal information bottleneck method aimed at ex- plaining the decision-making process of vision-language pre-trained models by compressing task- irrelevant information to highlight key predictive features. However, this approach introduces ad- ditional complexity, which will be analyzed further when discussing the IBP theory. Similarly, COCOA (Lin et al., (022) extended Integrated Gradients (IG) to multimodal tasks by incorporating positive and negative sample pairs in its loss function, but this requires sampling additional relevant examples, introducing extrancous information that may not be directly relevant to explaining the current sample.
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Other methods like TEXTSPAN (Gandelsman et al. 2023) and Hossain et al. also suffer from sam- ple dependency. TEXTSPAN requires constructing a specific text set to calculate similarity with the image, limiting its scope to predefined sets, while HHssain et al.] relies on selecting training data samples based on 1.2 distance in the embedding space, which is not always feasible in prac- tical settings. LICO (Lei al.) 2024 attempts to create an interpretable model by retraining it to maintain feature relationships between text and image, but this results in explaining the newly trained model rather than the original one, and randomness is introduced through batch sampling. FALCON (Kalibhat et al. 2023) explains each dimension in the feature space by finding images that highly activate specific feature, but this approach does not provide explanations for individual samples, limiting its applicability. Overall, many of these methods face challenges such as reliance on additional samples, randomness, or structural dependencies, making them less suitable for clear, direct explanations of pre-trained models.
3 PRELIMINARY
3.1 PROBLEM DEFINITION
Following the setup of CLIP (Radford et al. 2021 a trained MITR model can be defined as follows: let fr F" - Rd denote the image encoder, which transforms an input image æ E R" into a d- dimensional image representation; fr RT + Id denote the text encoder, which transforms an input text 2T E R. into a d-dimensional text representation. We can use cos (ffr((z)))))))))))((()) fr(rr)
to evaluate the matching performance between the visual and textual modalities. Additionally, the representations can be directly applied to downstream tasks (Sanghi et al. [022] [Zhou et al. [202)) In the following, we use f to represent either Jr or fT. By substituting I with Sr, we obtain the results associated with the image modality, and similarly, can derive the results for the text modality.
For L-layer neural network, we can decompose it into the concatenation of two neural networks fi-l o (x) at the (-th layer. For ease of expression, we use 2 = f fl-((z) to represent the latent feature of the intermediate layer.
Our goal is to construct an interpretability method A that yields A(x) E R The larger the value of A, the more important that dimension for the representation.
3.2 THE INFORMATION BOTTLENECK PRINCIPLE
The information bottleneck principle Tishby et al. 2000), based on information theory, introduces the bottleneck to control the amount of information passing through it, aiming to find the minimal feature encoding that retains the least amount of information from the original sample while preserv- ing the necessary information for a given task. For ease reading and understanding, we provide brief explanation and simplify the notation, with The goal of the information bottleneck principle is to construct an optimization function and find the optimal parameter A:
detailed analysis provided in Appendix A
X = max m Y) (1;;,;;
(1)
where T(x, Y) (x) - Hxx = H(Y) - HY]x)) represents the mutual information between events and Y. which can be interpreted as the reduction in uncertainty about event æ after ob- serving event Y. Intuitively, the stronger the correlation between the two, the greater the reduction in uncertainty, and thus the larger the mutual information. Here, I represents the input sample, represents the encoding of x, which can be understood as the extracted features, and Y represents the given task. controls the size of the bottleneck. We emphasize the Key Point 1: represents the value of À when the mutual information between the encoding and the task is maximized while minimizing the correlation with the original sample (i.e., extracting as few features as possible). The optimization process follows (Schulz et al. 2020).
4 METHOD
In this section, we first deconstruct how the Information Bottleneck Principle extracts the impor- tance distribution of sample features and analyze the shortcomings of applying this theory to the
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interpretability of deep learning. We then introduce Narrowing Bottleneck Theory, which is rigorously derived and applied to the interpretability of Multimodal Image- Representations.
4.1 ANALYSIS OF THE INFORMATION BOTTLENECK PRINCIPLE (IBP)
Several works (Wang al. 2023 Schulz et al. 2020h have applied the IBP to the interpretability of neural networks. Their approach typically involves inserting a Bottleneck layer at the I-th layer of the neural network, with Ri controlling the amount information in the 1-th layer feature (z). The optimal solution for A, based on Equation | is found through gradient descent iterations, and the control is achieved as follows:
210 ()) Aso= Zío + (1 A)E, N
Here, following the Grad-CAM approach (Selvaraju et al. 2017). the dimension of is split into two parts for discussion: i represents the spatial encoding, and represents the channel encoding (for instance, Rwxhxe, where w, h, and represent width, height, and channels, respectively. The x h portion is simplified i). The noise distribution follows N(,, and any distribution independent z can be used, but a normal distribution is chosen for computational simplicity. The parameters o² and μ be arbitrarily specified. When by Ee (A)))( (n, o²)). Intuitively, this measures the uncertainty allowed in the i-th feature dimension under the condition of the Key Point 1. If this dimension is very close to independent noise distribution of sample H, a small KL divergence implies irrelevance to æ, i.e., unimportance, and vice versa.
X", the importance of is given
dog.
and
(a) M2IB
(b) NIB (Ours)
(c) M21B
(d) NIB (Ours)
Figure 1: Illustrations of feature attributions produced by different methods.
While the IBP theory itself is solid, its application faces several limitations as following:
1. the optimization process introduces randomness, the calculation of [[2, Y) depends Zin (A), which involves sampling noise X. This results in numerous local optima in the optimization of Aie. leading variations between runs.
2. the hyperparameter in the optimization objective significantly influences the interpretabil- ity results (Wang et al. 2023). controls the trade-off between the two mutual information terms, allowing different task information result in entirely different explanations.
3. the KL divergence is always positive, preventing the explanation from reflecting negative properties (As shown in Figure our proposed method successfully distinguishes and ex- cludes negative properties from the explanation. In Figure [d] the M2IB method continues to highlight irrelevant negative features, such as the cat's face, even when the subject is dog. However, in Figure [b our method correctly ignores these negative properties, focus- ing more relevant, positive features, showcasing its improved attribution performance.)
4. the explanation results do not directly reflect the association between feature dimensions and I(2, Y) (the explanation is derived by optimizing Equation to obtain X, followed by computation).
To address the three above issues, propose the Narrowing Bottleneck Theory.
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4.2 THE NARROWING INFORMATION BOTTLENECK THEORY
In this section, we introduce three core theorems of the Narrowing Bottleneck Theory and propose our Narrowing Information Bottleneck Method (NIB) algorithm based on them.
We continue to introduce Bottleneck layer at the (-th layer and use to control the information flow, while the scalar serves universal update parameter for each layer, the flow information for each feature dimension is determined independently. More Details of À please refer to Appendix G
Zzc (A) X Fic + ~ o²)
(3)
However, unlike Equation 3 we use the same scalar À E R to control all dimensions of Addi- tionally, we assume that the noise follows a distribution with zero mean and variance o², and we eliminate the noise weighting factor 1 À (as long as the noise distribution is independent of 2, this modification does not affect the bottleneck's 's properties). 24c is deterministically obtained by the model, ensuring there is inherent randomness in its computation.
For simplicity, we present an equivalent form, where 1l represents the identity matrix:
8(A) = 2 E~ N(,, o
(4)
Theorem (Narrowing Information Bottleneck). Given S A1 < x2 S 1. we have sup (=(())), < sup (E()2), and when X have [(2(0),
Proof. We start by expressing the mutual information [ x)) as:
1(2,x)
En DKL
[P( [)][Q[=]] DRL. [P())[Q(()]
(5)
S E
DKL
p(2 +))l[(=3]
Given that P(((A)
, I2),, we can compute the difference between the mutual informa-
tion at two values of À follows:
1 1
sup I(E(A)), - sup T(3(A2), F.r
(AP
X²) 1|
(6)
2 0²
Since (Ap - X < 0, it follows that:
sup [(ā(Ai), sup ((E(A2),
This completes the key proof. Further details can be found in Appendix B
Theorem shows that by decreasing the value of X, we can reduce the mutual information between and its encoding, and when À = 1, [(((), x) reaches its maximum value. Since
the computation involves noise sampling, we aim to remove the randomness caused by Theorem which leads us Theorem
Theorem 2. When ²² 0, given 0 S A A2 we have:
sup I(3())), sup x( r2-30
(8)
In Theorem we demonstrate that the conclusion of Theorem
holds as a² tends to zero. Specifi- cally, a² 0, =(X) converges Az. In practical scenarios, due precision limitations, the two expressions will become indistinguishable, effectively eliminating any inherent randomness.
Theorem 3. Given the function I(2, Y), the following holds:
ΣΣ l
AT Y) 25 da (2)
Building on Theorem 2 we can adjust the value of À to control the size of x()()), When = 0, T(ā(A), x) is minimized, and when À T(3()), is maximized. Therefore, T(2(A), can be

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viewed as function of X, where the process from 1 to 0 corresponds to the bottleneck transitioning from fully open to completely closed. The importance A(z,) of 2, can be expressed as:
A(z)= - A (T(
(A)c(A) dx
(10)
(2(((1) OA
The total importance across all dimensions of 2 equals the loss in ( caused by narrow- ing the bottleneck from fully open to closed. Negative values are also allowed, as some features
may reduce ((² x). This process eliminates the need for balancing two mutual information terms, thus avoiding the introduction of the hyperparameter and preventing instability in the inter- pretability results.
For the design of =z) we follow the work of Wang et al. (2023), using cos (fr(xr), (T(())
as an equivalent replacement. It is worth noting that if we aim to obtain the attribution result for the image modality, (()()). z) becomes cos ff--1(2f
(xx)) fr (ZT)). Additionally, since in 2, represents the spatial encoding corresponding to the original encoding, the importance distribution of the original sample features A(x) can obtained by performing linear interpolation on À from
to 1, as described in (Wang et al. 2023 [Schulz et al. (2020. Theorem 2 Theorem 3] and the proofs
of the Sensitivity and Implementation Invariance axioms are provided in the Appendix.
EXPERIMENTS
5.1 MODELS AND DATASETS
In this study, we follow the experimental setup of M2IB (Wang et al., 2023). utilizing the pre- trained CLIP model with Vision Transformer (ViT-B/32) (Dosovisskiy 2020) as the visual en- coder. CLIP's joint optimization of image and text alignment has demonstrated outstanding per- formance in multimodal tasks. We conduct experiments on three different datasets: Conceptual Captions (Sharma et al. 2018), ImageNet (Deng et al. 2009), and Flickr8k (Hodosh et al.) 2013). Each of these datasets has unique characteristics, providing diverse visual and textual inputs for the model. Conceptual Captions is a large-scale image-text alignment dataset containing automati- cally generated image-text pairs, helping the model learn a shared feature space between vision and language. ImageNet, a classic image classification dataset, contains large number of annotated im- ages and a wide range of class labels, making it a standard dataset for training and evaluating visual models. Flickr8k is relatively small image-text alignment dataset consisting of 8,000 images and their corresponding natural language descriptions, commonly used to assess multimodal alignment image captioning and text generation tasks.
5.2 PARAMETER SETTINGS
We reduced the number of parameters required by the IBP-based method while retaining the core hyperparameters used during the generation of saliency maps, including the number of iterations (num_xteps) and the layer number.
num_stepss Number of Iterations The num__teps parameter refers to the number of iterations used during gradient optimization, and it primarily affects the precision. It determines how many updates are made to the feature maps in each layer during gradient backpropagation. A larger num muepsteps generally leads higher precision, as the model given more iterations accumulate gradients and refine attribution results. However, as the number of iterations increases, so does the compu- tational cost, necessitating balance between accuracy and efficiency practical applications. In our experiments, num sleps is set to 10, which has been experimentally verified to provide higher precision result while maintaining relatively low computational overhead.
Layer Number The layer number refers to the identifier of the specific layer chosen from the neural network model as the bottleneck layer. this study, we selected the 9th layer (layer number = 9), indicating that we extract the hidden states from the 9th layer for generating saliency maps. The choice of this layer is motivated by the fact that intermediate layers typically contain rich contextual information, reflecting both low-level features and some high-level abstract representations. Specifically, using the hidden states from the 9th layer allows us to capture the model's 's intermediate features, avoiding the level signals from early layers or the overly abstract
layer number:
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representations from deeper layers. The feature maps from this layer have been shown in practice to effectively support the generation of saliency maps, striking a balance between feature detail and semantic representation.
5.3 EVALUATION METRICS
In the evaluation of attribution algorithmss, traditional insertion score and deletion score metrics rely on task-specific confidence outputs. However, as our experiments do include task labels or confi- dence information, incorporating downstream task outputs would weaken the generality of the inter- pretability methods. Additionally, the metric ROAR+ (an Extension of ROAR (Hooker al. 2019). which requires retraining the model after removing key features, incurs high computational cost, particularly when dealing with complex models and large-scale datasets, significantly increasing time and resource consumption. For these reasons, we reference two model-output-basee evaluation methods proposed by Wang et al. (2023): Confidence Drop and Confidence Increase (Chattopad- hay et al. 2018), to evaluate the performance of attribution algorithms.
The Confidence Drop and Confidence Increase are evaluation metrics used to assess the effective- of attribution methods. The former measures whether model performance decreases when less important features are removed, with the ideal scenario being that only high attribution scores are retained and the removal of other features does significantly impact performance. lower value of Confidence Drop indicates better performance of the attribution method. The latter evaluates whether removing noisy information from the input enhances the model's confidence, with the ex- pectation that the removal of irrelevant features should increase the model's ss confidence. A higher value of Confidence Increase indicates better performance of the attribution method. Both metrics serve to gauge whether the attribution method effectively identifies and preserves important features while mitigating the impact of noise.
5.4 BASELINE
We compare proposed Narrowing Information Bottleneck (NIB) method against several well- established attribution techniques to evaluate its effectiveness. The baseline methods include M2IB (Wang al.) 2023). RISE (Petsiuk al., 2018), Grad-CAM (Selvaraju al.) 2017). the method by Chefer et al. (2021), Saliency Maps (Simonyan 2013 MFABA (Zhu et al. 2024c). and FastIG (Hesse al. 2021).
5.5 RESULT
Table 2: Performance comparison of the proposed NIB method with existing attribution methods across three datasets: Conceptual Captions, ImageNet, and FlickrSk. The evaluation metrics in- clude Image Confidence Drop, Image Confidence Increase, Text Confidence Drop. Text Confidence Increase, and Frames Per Second (FPS). Lower confidence drop and higher confidence increase indi- cate better performance, while higher FPS reflects better computational efficiency. NIB consistently achieves superior performance in both accuracy and efficiency across all datasets.
Dataset
Method
M21B
RISE
Grad-CAM
Chefer et al.
2021
SM
MFABA
FastIG
NIB (Ours)
ling Conl Drop
1.1171
1.4197
4.1064
2.0138
10.4351
10.1878
10.5117
0.9439
Conceptual Captions
Text Conl Drop 1
Ting Conf Incr
1.706
39,3
0,8002
28.8
1.7994
20.2
0.9333
33.65
1.0723
2.95
1,0503
2.6
0.9718
2.9
0.2705
42,5
Text Conf Iner
37.4
43.95
34.4
45,3
40.05
36.25
41.25
43.95
FPS 1
0.6621
0.1
1.1666
1.272
0.928
0.2494
0.9384
1,5817
Ing Conf Drop
2.5483
1.6636
S.0242
4.7905
0.9012
Ing Conf Incr
49.4
54
33.9
44
16.4
12.7
16.9
$3.1
ImageNet
Conf Drop
2.6424
1.6732
0.4193
Text Conf Incr
25.4
46.8
25.7
29.9
33,1
28.5
34,8
56.1
f
2.31155
Img Conf Drop
1.4731
3,01
5.1869
2.6214
12.154
12.07
12.2244
1.4495
Ing Conl Incr
28.1
5.7
13.6
26.8
0.1
D.1
28.1
Flickurkk
Text Conf Drop
2.0783
0.8914
2,1823
1.362
1.0797
1,1551
1,3098
0.4562
Text Conf Iner
34.7
46.4
34.2
42.6
45,9
42.6
43,9
55.3
FPS
0.7397
0.1076
1.958
2,4601
1,3973
0,2748
1,3944
2.1995
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The performance of our proposed NIB (Narrowing Information Bottleneck) method is compared with several existing attribution methods, including M2IB, RISE. Grad-CAM, and others, across three different datasets: Conceptual Captions, ImageNet, and Flickråk. The evaluation is based on four key metrics: Image Confidence Drop, Image Confidence Increase, Text Confidence Drop, and Text Confidence Increase. Additionally, the computational efficiency is assessed through Frames Per Second (FPS).
On the Conceptual Captions dataset, NIB demonstrates superior performance with an Image Con- fidence Drop of 0.9439, outperforming M2IB by 0.1732 units and Grad-CAM by 3.1625 units. Similarly, NIB achieves an Image Confidence Increase of 42.5, surpassing Grad-CAM by 22.3 units and RISE by 13.7 units, reflecting its strong ability to improve model focus by removing irrelevant features. For text-hased metries, NIB shows a notable improvement Text Confidence Drop, with 1.4355 unit advantage over M2IB and 1.5289 unit gap with Grad-CAM. In terms of computational efficiency, NIB achieves the highest FPS of 1.5817, providing a substantial performance boost over RISE and other methods.
In ImageNet dataset, NIB maintains leading position, with the lowest Image Confidence Drop (0.9012), outperforming M2IB by 0.2603 units and Grad-CAM by 1.6471 units. Additionally, NIB achieves the highest Image Confidence Increase of 53.1, showing 19.2 unit improvement over Grad-CAle and a 3.7 unit improvement over M2IB. For text metrics, NIB continues to excel with the lowest Text Confidence Drop (0.4193), representing a 2. 1825 unit gap over M2IB and a 2.2231 unit gap Grad-CAM. The FPS score for NIB is also competitive 2.4481, showing high efficiency in real-time applications.
On the Flickr8k dataset, NIB achieves the lowest Image Confidence Drop (1.4495), only slightly better than M2IB by 0.0236 units, but significantly outperforms Grad-CAM by 3.7374 units. In terms of Image Confidence Increase, NIB ties with M2IB 28.1, while exceeding Grad-CAM by 14.5 units. NIB also leads in Text Confidence Drop. with score of 0.4562, outperforming M2IB by 1.6221 units and Grad-CAM by 1.7261 units. The computational efficiency of NIB remains high, with an FPS of 1995, reflecting its ability to maintain high-speed performance compared to slower methods like RISE.
In summary, the proposed NIB method consistently outperforms existing attribution techniques across all datasets, providing better attribution accuracy and computational efficiency. The improve- ments in both Confidence Drop and Confidence Increase metrics demonstrate NIB's capability to identify key features and remove irrelevant ones, enhancing the interpretability and robustness of multimodal models. Please the attribution results images in the GitHub repository.
ABLATION RESULT
6.1 ABLATION STUDY OF num steps
Table 3: Ablation study results on the num steps parameter, comparing different values (5, 10, 15, and 20) across three datasets: Conceptual Captions, ImageNet, and Flickr8k. The evaluation metrics include Image Confidence Drop, Image Confidence Increase, Text Confidence Drop, and Text Confidence Increase.
numm steps
Img Conf Drop !
Ing Conf Iner
Text Conf Drop
Text Conf
0.9386
42.4
0.2056
43.4
Conceptual Captions
10
0.9439
42,5
0.2705
43.95
0.9424
42.75
0.3688
44.9
20
0,9378
43.05
0.4701
44,3
0.9554
51.5
0.3164
56.7
10
0,9012
53.1
0.4193
56.1
Imagenet
15
0,9558
53.4
0,4563
56.4
0.9691
54.3
0.4957
56.4
1,4547
26.7
0.3766
54.1
10
1.4495
28.1
0.4562
55.3
Flickrök
1.4443
26.7
0.6222
20
1.4504
27.3
0.8326
52.5

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Published as a conference paper at ICLR 2025
In the ablation study of the num_steps parameter, investigate the impact of varying the number of optimization iterations on the performance of our proposed NIB method. As shown in Table 3 conducted experiments with num steps values of 5, 10, 15, Target Layer Number fixed 9, and 20 across the three datasets.
The results indicate that the number of steps increases, there is trade-off between attribution accuracy and computational cost. For Conceptual Captions, increasing num_steps from 5 to 20 slightly improves the Text Confidence Drop but shows diminishing returns after num_steps 10, with only minor improvements in accuracy but a noticeable increase in computational overhead. Similar trends are observed in ImageNet and Flickr8k, where the best performance terms of Image and Text Confidence Drop occurs at num steps 10. Beyond this point, the gains are marginal, and the results suggest that setting num steps to 10 provides an optimal balance between accuracy and efficiency.
6.2 ABLATION STUDY OF target layer
Table Ablation study results on the target layer parameter, comparing layers 6, and across the Conceptual Captions, ImageNet, and Flickr8k datasets. The evaluation metrics include Image Confidence Drop, Image Confidence Increase, Text Confidence Drop, and Text Confidence Increase.
Dataset
larget layer
Ing Conf Drop | Ing Conf Iner
Text Couf Drop |
Text Couf Iner
Conceptual Captions
0.8616
1.2758
6
0.8514
43,55
0.9867
40.1
0.9439
3
0.6889
57
2.3727
31.9
ImageNet
6
0.7793
56.
2,4207
32.5
0.9012
53.1
0.4193
56.1
3
1,3022
26.5
1,5068
43.1
Flickr8k
1.2875
28.3
1.1981
46.9
1.4495
28.1
0.4562
55.3
In the ablation study of the target parameter, we explore the impact of selecting different layers for generating saliency maps. Specifically, evaluate the performance of layers 3, 6, and 9 across the Conceptual Captions, ImageNet, and Flickr8k datasets, with num_steps fixed at 10.
The results in Table 4 reveal that layer generally yields the best performance across all datasets. For Conceptual Captions, layer achieves the lowest Text Confidence Drop (0.2705) and the high- est Text Confidence Increase (43.95), indicating that the saliency maps generated from this layer provide the most accurate and interpretable attributions. Similarly, in the ImageNet dataset, layer 9 performs well, with moderate Image Confidence Drop (0.9012) and the highest Text Confidence Increase (56.1), demonstrating that it effectively captures important features for both image and text alignment.
In contrast, selecting carlier layers (3 and 6) results in higher Confidence Drop scores, particularly in the Text Confidence Drop metric, suggesting that these layers lack the necessary high-level semantic information. Therefore, the results indicate that layer strikes optimal balance between capturing rich feature representations and providing interpretable attributions, making it the most effective choice for generating saliency maps in the NIB method.
7 CONCLUSION
This paper introduces the Narrowing Information Bottleneck Theory (NIBT) to address the chal- lenges of randomness and hyperparameter sensitivity in explaining multimodal models like CLIP. By re-engineering the traditional Bottleneck method, NIBT improves interpretability for both image and text representations. The proposed method demonstrates superior performance in terms both attribution accuracy and computational efficiency across multiple datasets. These advancements contribute to a more transparent and reliable interpretation complex multimodal tasks, paving the way for broader applications of explainable AI in high-stakes environments.
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CODE OF ETHICS AND ETHICS STATEMENT
All authors of this paper have read and adhered to the ICLR Code of Ethiigturing the research, development, and writing of this work. We affirm that this paper complies with all ethical guidelines outlined in the code. No part of our research involved human subjects, and there are no significant concerns regarding privacy, fairness, or potential conflicts of interest. All datasets and methodolo- gies used are publicly available, and no sponsorship influenced the content or findings of this work.
Additionally, the interpretability methods proposed in this paper aim to improve model transparency and are intended for enhancing the trustworthiness of Al systems, especially in sensitive domains such as healthcare. We believe that our contributions will support ethical Al deployment by making models more interpretable and accountable.
REPRODUCIBILITY STATEMENT
We have taken multiple steps to ensure the reproducibility of our results. The detailed descriptions of the models, datasets, and training protocols used in our experiments are provided in the main text. Specific hyperparameters, including the number of iterations and selected layers for saliency map generation, are also reported Section 5.2. Furthermore, the code for implementing our proposed Narrowing Information Bottleneck Theory (NIBT) and the associated datasets available in the Anonymous Repository These resourees should enable the community to reproduce our findings and apply the methods their own work.
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9167 hr--ppamaa.-nnnnnaeeaaaaaaaaeeaa pdf
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A PRINCIPLES OF INFORMATION THEORY
A.1 PROPERTIES OF MUTUAL. INFORMATION
Non-negativity
I(XY)) 2 0
Symmetry
I(XY)) I(Y;))
3. Relationship with Conditional Entropy and Joint Entropy
[(X; Y) = -X(X) H(X
=H(Y) H(]
+ =
-
- -XX)))
- H(
4. Relationship with Kullback-Leiller (KL) Divergence
I(XY)) Σ p(v) Σ p(xly) loga p(aly)
Σr(v)
p(x)
Σ p(v) DEL (p(x)))loo))
By ([((((())))))(()))
B PROOF OF THEOREM
1|2, 2] = E. [DKL[P(2 [>]]|(
= -f p(x) (f p( x)) log p(ž xx)
dī dx
p(=)
p(ž x) ((2)
plu, Σ) log
dādr
((2) ((2)
11z x) dādx + / p(x, 2) log
((3)
p(a, 8) log
dādx
(τ)
p(3)
p1 x)
dēdr | M(3) (fple 2)drr log
((3)
p(a, 2) log
(())
p(2)
E, DKL [P x))
(3)]] (K3(P(3)((3)
E, [Dk.. (P(: [=)]]
Given this, we can simplify the final result as:
((ā,x) = E. [KL (P(2 x))(Q(z)) DKL (P())
s Es DKL (((2) =))[((a))]
=(A) = + e~ N(0,021)
equivalent to:
(A) >22o te, E~ N(0,00
Given that:
P( x) N(22,, 0TT)
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Published as a conference paper at ICLR 2025
let Q(2) N(0, Note that Q(2)'s covariance matrix can be o²I since activations after linear or convolution layers tend to follow a Gaussian distribution (Klambauer et al. 2017).
(Pi, 22) (I)) = trr (M + (p - -0- - 0) - x c]
Since the covariance matrices of P(ž x) and 0(0,001) are the same, (((-EE) = x and
det((2) 0, the remaining term simplifies to: det (E)
log
1
Dkt, (p( (1)) = -z ||l
Thus, we have:
1 o² ||
sup I(2,x) = E,
Given that P and Q follow normal distributions with means up and Ma, and covariance matrices Ex and Eg. respectively, we use the following KL divergence formula:
1 114p Mg) Eg (Mp Mg) + tr( Σp) nl
DkL(p())ll((z)) sup ((i())...) sup ((3(A2), x)) Ex
1 1
(Af
X2) ||
2 σ²
1
1
Ex
(Af
A2) 1|
2
Since Å1, A2 [0, 1] and d1 X2, have:
sup ((E(AI), sup ((5())),
Thus, Theorem is proven. When = 0, we have:
P(ε(0) x) = o20,
which is the same Q(2), leading to:
DKL (P((())
Hence, I(²(0),z) = 0.
C PROOF OF THEOREM 2
When a + 0, we have:
1 (X
Ex
Aσ) 1|ul
0, X X/ 0
02
Thus, Theorem 2 proven.
D PROOF OF THEOREM 3
The proof follows the standard method of change of variables in integral calculus. Detailed steps be found in textbooks on calculus.
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Published as a conference paper at ICLR 2025
E PROOF OF SENSITIVITY AXIOMS
ΣΣع (T(E()),)) dE dd 0x
()))) -5
(3(0),
(11)
(A)
As shown in Equation [T] the total importance across all dimensions of 2 corresponds to the de- crease in ((E(A),Y) Y) as the bottleneck narrows from fully open to fully closed, thereby satisfying the Sensitivity Axioms.
F PROOF OF IMPLEMENTATION INVARIANCE AXIOMS
By applying the chain rule, the proposed method inherently satisfies the Implementation Invariance Axiom.
G DETAILS OF X
Although À remains consistent across dimensions within layer, the actual updated values vary depending on the magnitude of each feature. For example, a feature has a magnitude of 8 and is set to (1/4), the final updated value will be 2. In contrast, if another feature dimension has magnitude of 6, the updated value will be 1.5. These variations ensure that our theoretical properties hold and that the bottleneck effect is applied dynamically based the specific characteristics of each feature.
H SENSITIVITY OF M21B TO THE 3 HYPERPARAMETER
Table 5: Effect of the |8 Hyperparameter on Confidence Metrics for the M2IB Method Across Dif- ferent Datasets
Dotaset
Conceptual Captions
Imagenct
Flickräk
Ing Conf
Ing Conf
Text Conf
Texi Conf
Ing Conf Drop
Img Conf Incr
Text Comf
Text Conf Incr
Ing Canf
Ing Cont
Teat Coni
Text Conf
Drop
Incr
Drop
Incr T
Drop
Drep
Incr
Iner
0.01
0.8738
38.65
0.9779
44
0.835
$2.6
1.1897
41.7
1.2544
27.3
1,1789
0.03
0.9144
39.2
0.959]
46.35
0.138
51.8
1.5526
32.6
1,282
28
1.174
47.6
0.04
0.943
39.15
1.077
45.4
0.9344
51.5
1,7488
30.8
1.3025
28.6
1,2631
47.7
0.05
0.9699
38.95
1.1631
44,7
0.9929
50.8
1.8966
31.4
1.3266
29
1.3423
46.4
0.06
1,0003
38.35
12813
42.9
10387
50
1.9928
29.9
1.3525
28.6
1.481
1.3853
1,5843
L.6227
1.4126
1,7559
0.09
1,0918
38.3
1,5014
39.15
1.1644
50.4
2.3936
25.9
L.411
28.5
1.8622
37.9
0.1
1.1244
38.4
1,7059
37.4
1020
49.5
2.5389
24.7
1.4731
28, 1
2,07833
34.7
0.2
1,4748
35,85
2.42015
27.85
1,899
45.9
3,631
18.3
1.8176
27
2,8446
26.4
3,0958
2,3452
28.4
2.9112
22.8
1.943
39.9
4.4614
12.4
3,0238
1999
3,2889
21.7
0.6 2.5184
25.95
2.9523
22.6
1.9561
38.8
4.5033
12.2
3,2089
18.2
3.5116
21.6
0.7 2.6748
25.3
2.9737
22,4
2.0337
37.5
4.5393
12.
3.549
16.4
3.3198
21.7
088
2.8316
23.9
2,9863
22.35
2.0717
36.7
4.5558
12.1
3,7771
15.3
3,3254
21.7
4.5649
As shown the table the hyperparameter has significant impact on the performance of the M2IB method, indicating that the method is sensitive to variations in 3. As 3 increases, the values of both Image Confidence Drop (Img Conf Drop 4) and Text Confidence Drop (Text Conf Drop J) increase noticeably across the Conceptual Captions, Imagenet, and Flickr8k datasets. This demon- strates that grows, the model's performance deteriorates on these metrics. Similarly, the values of Image Confidence Increase (Img Conf Iner 1) and Text Confidence Increase (Text Conf Incr t) decrease as f increases, further illustrating the influence of this hyperparameter on model behavior.
For instance, when 3 increases from 0.01 to 0.9, on the Conceptual Captions dataset, the Image Con- fidence Drop rises from 0.8738 to 2.9715, and the Text Confidence Drop rises from 0.9779 to 2.9947, while the Image Confidence Increase decreases from 38.65 to 23.25, and the Text Confidence In- crease decreases from 44 to 22.15. This trend is consistent across other datasets, particularly when is larger, leading more pronounced performance degradation. Therefore, it can be concluded that the M2IB method is highly sensitive to the 3 hyperparameter, and tuning 3 has a substantial effect on the confidence metrics of the model.
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Table 6: Expanded the scope of num num_steps ablation study
Dataset
mum
largel layer
Img Conf Drop
Img Conf Iner
Text Conf Drop
Text Conf Incr
3
0,9649
42.5
0,2066
43.5
Conceptual Captions
0.9424
43.2
0.2409
43.2
13
0,9422
42.8
0.3268
44.75
18
0.9408
42.9
0.4288
45
3
0.9698
51.7
0.3746
56.3
8
0,9438
53
0.4046
56.9
NetgeNet
13
0,9506
53
0,4444
57.3
0.9648
53.9
0.4935
3
1.4636
25.5
0,3875
$1
1.4526
0,4324
Flickr8k
13
1,4437
26.5
0.5525
53.6
18
1.4463
27.1
0.7381
53
Table 7: Expanded the scope of larget_layer ablation study
Dataset
num steps
target layer
Img Conf Drop
Img Conf Iner
Text Conf Drop
Text Conf Iner
0.8577
41.95
1.1717
0.8338
1,2319
37.2
10
0,8106
43,95
1.5805
34,5
Conceptual Captions
10
0.8514
43,55
0.9867
40.1
10
0.8911
43.75
0.8798
39.4
10
0.8898
41.6
0,3655
43.75
10
2
0,7062
54.5
2.1586
33.7
0.72
55.1
2.625
0.7906
3.496
ImageNet
10
0.7793
56.
2.4207
32.5
10
7
0.8931
54.8
1.4745
47.4
10
0.9258 8
52.7
0.985
49.8
10
2
1.2641
28.1
1.2748
44.4
1,283
27.7
1,4821
40.9
1.247
2.0031
36.5
Flickr8k
10
1,2875
28.3
1.1981
46.9
10
1.3609
28.6
1.2775
43.1
10
1.2881
28.8
0.9316
46.3
I EXPANDED ABLATION STUDY OF num_steps AND target_layer
We expanded the scope of our ablation studies with additional results, as shown in Tables 6 and in the supplementary material, to provide a more comprehensive analysis of hyperparameter inter- actions.
Table 8: Forward and backward passes of NIB compared to other methods
Method
Forward
Backward
NIB
12
10
RISE
301
0
Grad-CAM
Chefer et al.
SM
0
MFABA
10
M2IB
22
20
FastGG
0
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plane on the
Figure 2: Attribution result of RSICD dataset
J COMPUTATIONAL EFFICIENCY
As outlined in Table NIB achieves superior efficiency in terms of forward and backward passes compared to other methods. The efficiency of our method scales independently of model complexity and dataset size, as demonstrated in our evaluation.
K ATTRIBUTION RESULTS
To provide further insights into generalization, we have generated attribution examples using the RSICD remote sensing dataset. Figure indicates that the proposed method could generalise effec- tively to other domains.
18


Reviews:
[{"review_id": "HGn8t2UHlh", "reviewer": "Reviewer_bPyU", "summary": "The authors squeeze the information for classification through a bottleneck - in essence they are trying to reduce to the most important features. Importantly, it also shows negative and positive features for classification rather than just positives. To do this, it is trialed on 3 popular datasets, conceptual captions, imagenet, and flickr8k.\n \nThe method outperforms other interpretability methods on confidence drop, e.g how much model confidence decreases when good features according to the interpretability method are removed, and confidence increase, e.g how much model confidence goes down when negative features are removed.", "questions": "Can the authors  show interquartile range of performance outcomes in Table 2? in addition, other image examples. whilst the method clearly outperforms the others on average, it is not clear if it is robust.\n Even in the example shown with the image of the dog and cat, it responds strongly with the corner of the image, which is not interpretable.\nSimilarly, how does the method respond when the image is cluttered with more information, or the relevant information makes up a much smaller total proportion of the image. More examples are needed.\nChoice of layer number, can you justify theoretically why layer 9 has been chosed.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "While numerous interpretability methods exist, they cannot cope well with the multimodality od data.\nThe proposed methods addresses this issue, with 9% improvement for text interpratability, and 58% improvement fo image interpratability.\nThe proposed method also eliminates randomness an hyperparameter dependency.\nAlso, current methods stem from gradient analysis, which introduces additional challenges.", "weaknesses": "I am curious why the representation of both images and text is of the same dimension, d\nProof of Theorem 1 is obvious, so it should not be a theorem.\nTheorem 2 is basically a consequence of Theorem 1. It should not be a theorem. Both are nasically Remarks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nBuyFWhJX8", "reviewer": "Reviewer_bWWD", "summary": "The paper addresses the challenge of improving interpretability in Vision-Language Pre-trained Models, such as the multi-modal model CLIP (Contrastive Language-Image Pretraining) which maps images and texts into a shared embedding space. Existing interpretability methods, designed for unimodal tasks, are generally insufficient for explaining the complex relationships between visual and textual modalities. To assess the interpretability in the model CLIP, Wang et al (2023) recently introduced the Multi-modal Information Bottleneck (M2IB) approach, aiming to find attribution parameters that maximize the likelihood of observing features of one modality given features associated with the respective other modality.\n\nFollowing from Wang et al (2023), here, the authors propose the Narrowing Information Bottleneck Theory (NIBT), a new interpretability approach that redefines bottleneck methods in information theory. NIBT targets the randomness and hyperparameter dependency associated with previous approaches, such as M2IB, to deliver more deterministic interpretability outcomes. The method includes a mechanism to pinpoint negative features that adversely impact model predictions, aiming to further enhance the interpretability.\n\nThrough simulations on the datasets Conceptual Captions, ImageNet, and Flickr8k, the authors state that NIBT significantly improves interpretability and processing speed compared to state-of-the-art methods.", "questions": "Q1. You write that \"the interpretability methods proposed in this paper aim to improve model transparency and are intended for enhancing the trustworthiness of AI systems, especially in sensitive domains such as healthcare\" (lines 540-542). You chose to present results on three datasets. Are these datasets sufficiently diverse to reflect real-world multimodal scenarios, especially in domains where interpretability is crucial, like medical imaging? Why did you not choose at least one medical imaging dataset like the M2IB paper?\n\nQ2. Could you please explain why you present the illustration of your method compared to the M2IB method in the paper (and compared to other methods in your code) on a unique image of a cat and dog. Why did you choose two simple and short text captions to illustrate your method? \n\nQ3. The paper claims an average improvement of 9% in image interpretability and a 58.83% increase in text interpretability. The important increase in text interpretability is striking and suggests that NIBT offers substantial benefits. Could you please justify why the improvement in text interpretability is so much higher than in image interpretability? Are there inherent differences in how text and image features are handled by CLIP that NIBT is better suited for?\n\nQ4.  How does identifying negative features improve model interpretability in practical applications? For instance, could this lead to better debugging of models or highlight biases in the training data? Providing concrete examples would strengthen the argument for this feature. \n\nQ5. Are there scenarios where the method might fail?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 1, "presentation": 3, "contribution": 2, "strengths": "The paper introduces the Narrowing Information Bottleneck Theory (NIBT), a new interpretability method that redefines traditional bottleneck concepts from information theory. This approach departs from the randomness and hyperparameter sensitivity inherent in previous methods, enabling a deterministic alternative for multimodal representation interpretability.\n\nThe concept of attributing negative features—dimensions that negatively impact model performance—is original. This feature provides an innovative perspective on model understanding, distinguishing NIBT from other methods focused solely on positive attributions.\n\nThe paper provides theoretical derivations and mathematical proofs for its main claims.\n\nThe method is assessed across three datasets, including Conceptual Captions, ImageNet, and Flickr8k.", "weaknesses": "1. The Narrowing Information Bottleneck Theory (NIBT) is introduced to address limitations in previous bottleneck methods by providing a deterministic approach, aiming to reduce randomness and dependency on hyperparameters. The method states to use a single scalar λ to control information flow across all feature dimensions. This simplification is significant:\n\n1.1. The scalar λ as a universal control across all dimensions suggests a uniform bottleneck effect, which can be problematic if different features vary significantly in their contribution or sensitivity. In practice, applying a single λ across all dimensions might oversimplify the dynamics of feature importance, potentially reducing interpretability in models where feature contributions are heterogeneous.\n\n1.2. The noise distribution, ϵ∼N(0,σ^2), plays a role in regularizing the information flow by adding Gaussian noise to the controlled dimensions. This assumption is justified under the simplification of feature distributions. However, the authors should justify why the Gaussian assumption holds across different multimodal applications, as non-Gaussian feature distributions might invalidate this approach or reduce its robustness.\n\n\n2. Equations\n\n2.1. Equation 1 is well-grounded in information theory, with a clear goal of maximizing relevant information while minimizing redundancy with respect to the input. The critical factor here is the interaction between λ and β. While NIBT eliminates β, this raises questions about managing trade-offs between task-relevant and redundant information. The authors should address potential limitations of a fixed λ and how it impacts feature discrimination.\n\n2.2. Equation 2: The decomposition of features into spatial (i) and channel (c) dimensions adds specificity but introduces complexity. By normalizing the interaction with Gaussian noise across spatial dimensions, the method assumes consistency in spatial features' importance, which may not hold in multimodal contexts where image regions and text embeddings exhibit varied significance.\n\n\n3. Theorems \n\n3.1. Theorem 1: it claims that mutual information, I(z~(λ),x), is a monotonically increasing function of λ, with I(z~(0),x)=0. The proof relies on KL divergence and a Gaussian assumption. While monotonicity is proven under Gaussian noise, real-world feature distributions could violate this condition. The impact of deviations from Gaussianity on this monotonic relationship needs to be tested. Also, the assumption of independence between noise and features for bottleneck properties needs further validation, as dependency might arise naturally in multimodal models where spatial and channel-wise correlations exist.\n\n3.2. Theorem 2: the reduction of σ toward zero should be more justified. Practically, in cases of low signal-to-noise ratios, completely removing σ could reduce robustness. A discussion of trade-offs in interpretability and stability as σ approaches zero would help add clarity. Also, the claim that I(\\bar{z}(λ), x) becomes deterministic as σ decreases suggests a very controlled bottleneck. It is theoretically sound, but validating this deterministic behavior across different datasets (especially with diverse noise levels) is needed to confirm this claim.\n\n3.3. Theorem 3: equation is both novel and complex, stating that negative values represent features that reduce information relevance. The introduction of negative features could indeed improve interpretability by explicitly identifying detrimental features. The authors should show that these attributions correspond to irrelevant or misleading features across model types. Another remark is that the integral's dependency on λ suggests that the influence of each feature diminishes as λ decreases. Yet, this gradual reduction might fail for complex, highly nonlinear multimodal interactions. A closer look at cases where feature relevance changes nonlinearly with λ could show edge cases where the method’s assumptions do not hold.\n\n\n4. Qualitative Analysis: The paper includes one visual comparison with M2IB only, but a more detailed qualitative assessment would be beneficial. For example, showing more examples where NIBT succeeds and where it fails compared to other methods would provide a better understanding of the strengths and limitations.\n\nFor the interpretation of Figure 1, the authors write: \"As shown in Figure 1, our proposed method successfully distinguishes and excludes negative properties from the explanation. In Figure 1d, the M2IB method continues to highlight irrelevant negative features, such as the cat’s face, even when the subject is a dog. However, in Figure 1b, our method correctly ignores these negative properties, focusing on more relevant, positive features, showcasing its improved attribution performance.\" Looking at this unique visualisation, the method proposed seems to lack specificity and highlight areas of the image that are not relevant, compared to M2IB.\n\n\n5. Computational Efficiency: The authors emphasize the efficiency of NIBT, Yet the experiments lack a detailed breakdown of computational costs relative to model complexity and dataset size. Including this analysis would better illustrate the trade-offs between interpretability and computation.\n\n\n6. The paper does not address whether the differences in the results between the different methods are statistically significant. Adding statistical tests (e.g., t-tests or bootstrap analysis) would add rigor to the claims of superiority over baseline methods.\n\n\n7. Regarding the impact of negative feature attribution: The paper does not at the moment empirically validate whether the identified negative features are truly detrimental.\n\n\n8. The ablation studies on num steps and target layer provide useful insights, but they feel somewhat limited in scope. It would be useful to look at the interplay between num steps, target layer, and other model hyperparameter would give a more holistic view of how these factors influence performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LVp3xZUhDL", "reviewer": "Reviewer_Ycuc", "summary": "This paper presents the Narrowing Information Bottleneck Theory (NIBT) as a solution to the challenges of randomness and hyperparameter sensitivity in the interpretation of multimodal models like CLIP. The authors systematically summarize existing methods of Multimodal Image-Text Representation (MITR) interpretability and modify the traditional Bottleneck approach with NIBT, which enhances the interpretability of both image and text representations. The proposed method shows improved performance in attribution accuracy and computational efficiency across various datasets.", "questions": "1.\tThe proposed method NIBT claims no randomness. But the computation of z_{ic} still contains the sampling noise.", "limitations": "", "rating": 5, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1.\tThe paper systematically and theoretically analyzes the current MITR interpretability methods. The theory is based on information theory and seems reasonable. \n2.\tThe proposed NIBT is simple by replacing the univariate Gaussian noise in the bottleneck layer with the multivariate gaussian distribution to control all dimensions of the model hidden states.", "weaknesses": "1.\tThe proposed method is simple by changing univariate distribution to multivariate distribution, but it's unclear whether it will correlate with the limitations of IBP. The theoretical analysis and the proposed algorithm seem loosely connected to me. I don't see the proposed method as a direct result of the theoretical analysis.\n2.\tThe story is a little bit hard to follow. It would be better for authors to make the story clearer.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JOjaljxeik", "reviewer": "Reviewer_nFC1", "summary": "This paper tackles the challenge of enhancing interpretability in multimodal image-text models like CLIP, which are widely used in applications demanding high reliability, such as healthcare. The authors introduce the Narrowing Information Bottleneck Theory, a re-engineered bottleneck approach that aligns with modern attribution standards to improve interpretability. Experimental results demonstrate significant gains: a 9% improvement in image interpretability, a 58.83% boost in text interpretability, and a 63.95% increase in processing speed compared to state-of-the-art methods.", "questions": "- Can the authors clarify whether the current content will be revised to meet ICLR’s page limitations for the main text?\n- Have the authors considered adding experiments on healthcare or content moderation datasets to validate the method’s relevance for high-risk applications?\n- Given the limited dataset variety in the current experiments, could the authors discuss how the proposed method might generalize to other domains, even by qualitative analysis?\n- Would it be possible to revise Tables 2 and 3 to ensure consistent significant figures for improved readability?", "limitations": "", "rating": 5, "confidence": 3, "soundness": 1, "presentation": 2, "contribution": 1, "strengths": "- Clarity and Structure: The paper is well-organized and written in a clear, accessible manner, making it easy to follow even for readers who may be less familiar with the technical aspects of interpretability in multimodal models.\n- Code Accessibility: The authors have made their code publicly available, which greatly enhances the reproducibility of the research and provides valuable resources for the community to build on this work.\n- Mathematical Rigor: The theoretical formulae are meticulously derived and clearly presented, enhancing the paper’s rigor and allowing readers to grasp the technical foundation of the Narrowing Information Bottleneck Theory.\n- Comprehensive Literature Review: The paper thoroughly addresses related work, including important methods like M2IB, COCOA, and FALCON. This contextualization not only strengthens the theoretical foundation but also allows readers to understand how the proposed method improves upon existing approaches.", "weaknesses": "- Paper Length: The paper exceeds the ICLR25 page limit, taking more than 10 pages for the main text. According to the ICLR25 “Call for Papers,” submissions with main text on the 11th page are subject to desk rejection. The authors may need to condense certain sections to adhere to submission guidelines.\n- Application-Specific Relevance: Although the paper emphasizes the importance of interpretability for high-risk applications such as medical diagnosis and content moderation, it lacks application-specific experiments. No quantitative or qualitative evaluations on healthcare or content moderation datasets are included, which would provide critical insight into the real-world applicability of the proposed method.\n- Experiment Scope: The experimental evaluation appears limited. In contrast to the M2IB paper, which includes evaluations on diverse datasets (image captioning, radiology, and remote sensing), this paper only tests on standard image datasets. A broader range of datasets would strengthen the evidence for the proposed method’s versatility and robustness in high-stakes domains.\n- Table Formatting: The significant figures in Tables 2 and 3 lack consistency, which affects readability and may lead to confusion when comparing results.\n- Minor Typos:\n  - Line 44-45: “blackbox” should be formatted as ``blackbox’’.\n  - Line 322: “Appendix” is referenced without any supporting link, in contrast to Line 296, which does provide a reference.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)