Patronus: Interpretable Diffusion Models with Prototypes
Authors: Nina Weng
Aasa Feragen
Siavash Bigdeli
Venue: ICLR 2026 Poster (2026)
Keywords: interpretability
interpretable diffusion model
diffusion models
transparency
prototypical network
shortcut learning
bias detection
Primary area: interpretability and explainable AI

Abstract: Uncovering the opacity of diffusion-based generative models is urgently needed, as their applications continue to expand while their underlying procedures largely remain a black box. With a critical question -- how can the diffusion generation process be interpreted and understood? -- we proposed *Patronus*, an interpretable diffusion model that incorporates a prototypical network to encode semantics in visual patches, revealing *what* visual patterns are learned and *where* and *when* they emerge throughout denoising. This interpretability of Patronus provides deeper insights into the generative mechanism, enabling the detection of shortcut learning via unwanted correlations and the tracing of semantic emergence across timesteps. We evaluate *Patronus* on four natural image datasets and one medical imaging dataset, demonstrating both faithful interpretability and strong generative performance. With this work, we open new avenues for understanding and steering diffusion models through prototype-based interpretability. Our code is available at [nina-weng.github.io/patronus.github.io](https://nina-weng.github.io/patronus.github.io/).

Published as a conference paper at ICLR 2026
Patronus: INTERPRETABLE DIFFUSION MODELS WITH PROTOTYPES
Nina Weng, Aasa Feragen, Siavash Bigdeli Technical University of Demmark (ninwe, afhar, sarbi]]dtu.de
ABSTRACT
Uncovering the opacity of diffusion-basee generative models is urgently needed, their applications continue expand while their underlying procedures largely remain a black box. With a critical question - how can the diffusion generation process be interpreted and understood? we proposed Patronus, an interpretable diffusion model that incorporates a prototypical network to encode semantics in visual patches, revealing what visual patterns are modeled and where and when they emerge throughout denoising. This interpretability of Patronus provides deeper insights into the generative mechanism, enabling the detection of shortcut learning via unwanted correlations and the tracing of semantic emergence across timesteps. We evaluate Patronus on four natural image datasets and one medical imaging dataset, demonstrating both faithful interpretability and strong generative performance. With this work, we open new avenues for understanding and steer- ing diffusion models through prototype-based interpretability. Our code is available at wtw.-wwwwngituubboo/pattoono
INTRODUCTION
The generative capabilities of modern machine learning models, particularly diffusion models, have advanced significantly, enabling the creation of highly realistic samples that closely resemble real- world data. However, their opacity raises critical concerns, including bias amplification (Luccioni et al. 2023), unsafe content (Qu et al., 2023), and copyright violations (Vyas et al.] 2023). Their lack of transparency makes difficult to detect and mitigate these risks, highlighting a fundamental question: How can the diffusion generation process be interpreted and understood? Specifically, what visual patterns emerge during generation, where and when they appear, and to what extent they can be controlled. Addressing these questions is essential, only for improving generative ability but also for ensuring interpretability. transparency, and ethical deployment, aligning with regulatory frameworks such the EU AI Act.
Existing approaches to improving interpretability in diffusion-based visual generation typically fall into categories. The first relies on post-hoc analysis investigate how semantic information encoded in intermediate representations (Kwon et al. 2022 Lee et al. 2023 Park et al.) 2023 Haas al. 2024). However, this method is inherently retrospective and limited its ability provide direct control over generation. The second approach introduces additional encoder- semantic vectors for diffusion guidance (Preechakul which improves controllability, but often resulting in representations that are difficult to interpret. Moreover, these methods tend to capture global (e.g. face shape, pose) rather than fine-grained patterns (e.g. hair/make-up details, facial expressions). and the latter are crucial for interpretability.
al., [0222 Leng et al., 2023, Wang et al. 2023b),
To address these limitations, we aim to develop method that fulfills two key objectives: (1) embed- ding interpretability directly into the model architecture, thereby providing intrinsic transparency and climinating the need for post-hoc analysis of high-dimensional latent features; (2) moving inter- pretability step further from global to local semantic meanings and enabling the controllability.
In this work, we propose Patronus, an interpretable diffusion-based generative model integrated with prototypical network for semantic encoding (Fig- [p.. Our approach trains prototype features to capture localized patterns within image patches, and uses prototype activation vector to encode the presence of semantic information for diffusion guidance. This design effectively reduces the

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Published as a conference paper at ICLR 2026
Patronus interprets DDPM
(c) Overview Patronus framework
Learned prototypes (vieual conce
"what"
"where"
prototvpe acivation
"when"
X
Conditional DDPM
Image
"smmi""
0.0 0.5 1.0 1,5
"ourly hair"
Forward diffusion
"white collar"
timestep
Generated sample
P1
(b) Patronus detects shortcut learning
Prototype extraction encoder
P2
* "Black hair"
Hair color changed v Smiile also changed to non-smile?
W
Pm
Zn
((xa)
P
Prototype extraction and representation module
Figure 1: Proposed Patronus model. (a) Interpretability: By integrating a prototypical network as the encoder, Patronus learns semantic prototypes ("what") and explains the generative process by revealing where and when they emerge. (b) Diagnosis: Patronus could detect unwanted correlation (e.g., in this case hair color and smile) learned from training data. (c) Overview of Patronus: contains a prototypical network for protolype extraction and conditional DDPM for generation.
latent dimensionality while preserving sufficient semantic information. Patronus allows directly vi- sualizing the semantics of the learned prototypes by manipulating the condition signals on specific prototypes, thereby enhancing interpretability and enabling the diagnosis of shortcut learning. Be- yond that, Patronus further reveals when prototypes emerge during the denoising process, providing temporal insights and guiding more efficient editing.
Our contributions summarized follows:
We propose Patronus: An interpretable diffusion model for image generation which incorpo- rates prototypical network for prototype learning and representation, alongside a conditional guidance by prototype activation vector entirely without extra annotations.
We introduce a novel method for visualizing learned prototypes, revealing the semantic mean- ings of them and how they engaged in the generation process.
Through extensive experiments, we show that Patronus effectively captures semantically mean- ingful features within images, and achieves competitive latent quality and generation quality compared to SOTA methods.
We empirically demonstrate the potential of Patronus diagnose hidden bias by detecting shortcut learning, offering valuable tool for mitigating biases in generative models and pro- moting fairness in their deployment.
RELATED WORK
2.1 PROTOTYPE-BASHR INTERPRETABILITY
Our work is closely related to and motivated by ProtoPNet (Chen al. 2019). which builds inter- pretable deep learning frameworks by learning prototypes, intermediate representations of visually similar patterns between training and inference images.
Under such design, a key challenge is visualizing the learned prototypes. Li et al. (2018) used a decoder to reconstruct prototypes, which resulted blurry visualizations due to data ambiguity. ProtoPNet instead identified the closest matching encoded representations from the training set via distance comparisons. However, this approach cannot guarantee capturing the true representative mode of the distribution. Follow-up works (Donnelly et al. 2022: Wang et al. 2023a Ming al. 2019 Ghosal & Abbasi-Asl 2021) explore alternative prototype design but do not enhance visual- ization. We propose new method for prototype visualization that allow finding the most probable visual representation prototype. Additionally, the aforementioned studies only focus on classi- fication, our work focus learning and visualizing prototypes for generative models.
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2.2
INTERPRETABLE DIFFUSION MODELS
Recent work on explaining diffusion models has focused on the following two directions.
Semantic Interpretation of Internal Features. Diffusion models were traditionally viewed as lacking internal representations compared to other generative models. like VAE (Kingma & Welling
2013) and GAN (Goodfellow al. 2014). However, Kwon al. (2022) pointed out that diffusion models possess semantic latent space within the U-Net's 's intermediate layer. They leverage the bot- tleneck for semantic control over the denoising process and propose the asymmetric reverse process
to edit the image based on the discovered semantic meaning.
Building on this idea, researchers have sought to interpret the latent space of diffusion models by grouping the latent vectors with different noise schedules (Leee et al. (023). applying pullback met- rics to obtain meaningful local latent basis (Park et al.) 2023), and uncovering semantically mean-
ingful directions by both PCA and linear properties of the semantic latent space (Haas et al. 2024).
Other works (Si et al. 2024 Tumanyan et al. 2023) extract structural information from the latent space and use it for 121 translation prompted generation tasks.
This line of work focuses on post-hoc analyses of existing diffusion models, whereas our approach provides intrinsic interpretability by design. Moreover, while prior studies often rely annotated data or external classifiers to uncover the semantic latent space, Patronus learns prototypes in a fully unsupervised manner.
Autoencoder-baze based semantic feature extraction for guidance. An alternative way to enhance the interpretability of diffusion models is integrate an additional encoder extract semantic fea- tures for guidance. DiffAE (Preechakul et al. (022) pioneered this idea using a learnable encoder for latent semantics. Expanding on this, DiffuseGAE, (Leng et al., 2023) proposed group-supervised AutoEncoder to achieve better latent disentanglement, while InfoDiffusion (Wang et al.) (023b) re- duced latent dimensionality and enforced mutual information constraints for more effective learning.
While these methods extract global semantic features, Patronus instead captures local features through a prototypical network. Another key distinction lies in how the diffusion model is guided: Rather than direct semantic information, significantly reduces the dimensionality needed for diffusion guidance while preserving enough ca- pacity in prototype features to encode the same semantic information.
the prototype activation vector. This approach
3 PATRONUS: INTERPRETABLE DIFFUSION MODEL
Our proposed model, Patronus: Prototype-Assisted Transparent Diffusion Model, is illustrated in Fig. Dc. Designed to enhance transparency and interpretability in diffusion models, Patronus incorporates a prototype extraction and representation module [-botttm) This module learns patch-based prototypes within the image and computes similarity score for each prototype, which then used to condition the diffusion process (Fig. [p-top). We elaborate the details of the prototype extraction and representation module in Sec. and the conditional DDPM in Sec. 3.2 Furthermore, explain how transparency and interpretability are achieved, including sampling strategy and manipulations, in Sec. [.3] We detail the unconditional sampling strategies in Sec. 3.4
and furthermore prove that adding conditions does not degrade the denoiser training in Sec. 3.5
3.1 PROTOTYPE EXTRACTION AND REPRESENTATION
This module, inspired by ProtoPNet, consists of two key components: The prototype encoder transforms input images into patch-based feature representations. Utilizing the properties of convo- lutional neural networks (CNNs), each output neuron in the feature map corresponds to a specific patch of the input image, determined by the network's receptive field. This patch-based repre- sentation enables the model to focus on localized patterns and learn fine-grained prototypes. The activation vector is derived by calculating similarity scores for each learned prototype, based on the distance between encoded patches and prototypes, where higher scores indicate stronger matches.
This module works as follows: As shown in Fig. Mp. given an input image Zg, the prototype encoder extracts features f (zn) into tensor of shape H x W x D. In this work, the encoder is
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4-layer Conv-ReLU encoder. The network learns m prototypes in the latent feature space during training, denoted as P = {p, 1 each with the shape
x D Each prototype Pj can be interpreted latent encoding of a patch in the original pixel space. This patch, importantly, need not exist in the dataset but should lie within the plausible data distribution.
For the encoder output a prototype (1 x x D) can be interpreted as representing patch of zo. Thus, 2 can be decomposed into smaller regions as follows:
f each spatial region within that corresponds the same size as
{zi]== where n denotes the total number of patches encoded
in z. In our case, n = H x W.
To calculate the similarity between the encoded features z and the learned prototypes P, be- gin by computing the squared L2 distance between each spatial feature 2; and each prototype py: (z) = || p5112. Next, the minimum distance across the spatial dimensions is selected for each prototype: Enin. = mar(-dff, kernel size = (H, W)), where dł the set of distances for the jeh prototype across all spatial positions, and the kernel size aligns with the feature map's spatial dimensions and W. The sequence of minimum distances for all prototypes is converted into an
activation vector using log transformation: = log( df +1 where is small positive constant. dete)
3.2 CONDITIONAL DIFFUSION PROCESS
Denoising diffusion probabilistic models (DDPM) form a class of generative models that learn data distributions by iteratively denoising a noisy latent representation. The process involves (1) for- ward diffusion process, where Gaussian noise is progressively added to a data sample To over T timesteps, producing noisy latents vt, defined as
q( | xx-1) = Na Vortt-1 - -..)
(1)
Here, the marginal distribution of given 2o is:
( zo) = Naz vārzo (L-ā,)I);
where ā₁ Ih O₁ and a 3r, where 3; is the variance of the Gaussian noise added t. Furthermore, DDPMs rely on (2) a reverse generative process that removes noise given
po (x xx,) 1; (), Ea (zx, I)).
To enable conditional generation, modify the reverse process to be conditioned on the prototype activation vector 8. Therefore the updated reverse process is:
( Zt, s) .V (N t, 5),, Ex(x,, t)).
The training objective remains based on the standard noise-prediction loss used in DDPM. For given noisy sample F,, timestep l and noise e the model minimizes the loss:
Ladpm Ezo, &.c allee ca t, s)
(5)
where eg is the learned denoiser. the loss indicates. our guidance does not change the model's output -it it only encourages its reasoning to utilize prototypes for transparency.
3.3 TRANSPARENCY AND INTERPRETABILITY OF PATRONUS
The similarity score 8j quantifies the activation of the jeh prototype in a given input, indicating the presence of specific semantic patterns. The model thus conditionally generates samples guided by interpretable semantic information.
Visualizing learned prototypes. Integrating a prototypical network semantic meaning traction module brings inherent interpretability: Each learned prototype vector pj represents a patch in the image domain. In ProtoPNet, those patches are retrieved by greedily searching all candidate patches in the training set for the closest embeddings.
We argue that the learned prototypes do not need to correspond directly to specific training patches but should instead align with the overall distribution of the training data. To support this, we propose novel prototype visualization method with the following steps:
whose generalization Hg W1 Chen
2019 is straightforward.
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1. Compute the activation vector {8 for given sample 20, where s, represents the similarity score between Zy and the Jeh prototype.
2. For target prototype J, increase its similarity score to the plausible maximum while keeping all other scores unchanged. The updated activation vector s' {s) ( is defined
j + as: s'j max(ssx) if = J
8/:
Here, sx represents similarity scores from representa-
tive subset, constraining within plausible range.
3. Using the updated activation vector s' to sample new image x' conditioned on s'.
4. Identify the most activated patch z. in x' that corresponds to the target prototype J. This patch xaf serves as the visual representation pJ-
This method could also be used to visualize prototypes in other prototypical deep learning models.
Manipulation using the prototype activation vector. Manipulating images natural down- stream task for Patromus, as adjusting a specific prototype similarity score 8j and conditionally generating new sample allows us to effectively and semantically control the image content:
po(It-1 Te. (2 uo L, 5'), Ee( t))
(6)
Deterministic reverse process via DDIM sampling. Both the visualization of prototypes and their manipulation via activation vectors build on the DDPM sampling process. However, for stricter control over the stochasticity introduced by random noise, the Denoising Diffusion Implicit Models (DDIM) sampler provides an alternative approach. Here, the denoiser is given by pa p0 24,
(1 Mr; I),, with mean 14, / -ig + v 0: r² ( 8) and variance d² 172
1-
1-64 (1 ) By setting = 0.0, the process becomes deterministic, leading to the
following update for the reverse process: It-1 0 2o + /1 ā co s). Here, do (I) VI di co co t, s)).
the estimated denoised image, computed as f. Va
This DDIM sampling requires æ7, which represents the initial noise in the diffusion process. This could be obtained by performing a deterministic backward generative process, given by 21+1 va 2. + VI ā++1 co tt),
3.4 UNCONDITIONAL SAMPLING STRATEGY
For unconditional sampling, we train an auxiliary latent diffusion model (16,-1(8;,1) to sample During training, we first jointly optimize the prototypical encoder with the conditional DDPM; subsequently, the latent diffusion model is trained with the parameters of prototype encoder frozen.
3.5 ADDING THE CONDITION TO THE ORIECTIVE
In Patronus, the prototype encoder is jointly optimized with the denoiser. To show that this simulta- neous training does not degrade the generated distribution, affects the likelihood. For ease of derivation, we use the Evidence Lower Bound (ELBO) as an equivalent objective generalize denoising losses (Ho et al. 2020).
analyze how updating the condition
Proposition. Any ELBO-immrovie update for the encoder always leads to progress. Such an update either increases the conditional likelihood of the data under the model or reduces the KL divergence between the generated distribution and the underlying data distribution.
Proof. Let = 21. denote the forward noised latents generated from a fixed forward process q(> x), as is standard in diffusion models. For parameters 8, define the per-sample evidence lower bound (ELBO)
ELBO(2; s) = (og
log q(>) x))].
and the conditional likeliiel
log pa( s) ELBO( KT.(((= ax) po(= 1 xr, s))
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Consider update s' + s that satisfies ELBO s(+1) EzBO s²) for the data point
Then the following identity holds:
ELBO s Po 1 log (a s') = -
AKI(x),
(7)
where (KL(x) = (L(((z æ) ll pe(z x )f+ - (L(((2) <)) po x r,,
Given that the ELBO difference term is positive, Equation implies that one of the following (improving) outcomes must occur:
Conditional likelihood improves. (x)(2) S 0, then
(L(g(= a:) || po(= x s s1+)) <= KL(((= xx) pl (x,,,)))))
meaning that the model posterior Po(= x, 8 g more closely matches the forward distribution
((2 z). This reduces the gap between the ELBO and the true conditional log- lollleilihood, yielding a tighter variational bound.
Generated samples become more probable. If AKL(x) and considering that ELBO is also increased, then necessarily
po s 2 log po(r) |s)).
i.e. the model assigns higher probability to æ.
Hence, any update of the encoder that increases the ELBO either increases the conditional likelihood of the data or reduces the KL divergence between the model posterior and the forward process, guaranteeing progress in approximating the data distribution.
This analysis is inapplicable under alternative training objectives for latent condition (e.g., InfoDiff).
EXPERIMENTS AND RESULTS
In our experiments, we first assess the semantic meaning of learned prototypes (Sec. 4.1 through reconstruction, interpolation, extrapolation, and manipulation tasks. We also investigate prototype visualization and their consistency. We then analyze prototype quality and generation performance (Sec. 4.2 Finally, we explore how Patronus aids in diagnosing shortcut learning in diffusion models by identifying potentially unwanted correlations via prototype similarity scores (Sec. 4.4)
We use five datasets: Quantitative evaluation on FMNIST (Xiao et al. (017), CIFAR-IU 10 (Krizhevsky al. 2009). FFHQ (Karras 2019); qualitative analysis Che CheXpert (Irvin et al. 2019); and in-depth quantitative and qualitative experiments on CelebA (Liu et al. 2013). We use 100 prototypes for all datasets except FMNIST, which has 30. Prototypes encoded with shape of (1,1,
4.1 SEMANTIC MEANING OF THE LEARNED PROTOTYPES.
We firstly verify that the learned prototypes are semantically meaningful by the following analyses.
Reconstructing image semanties from prototype activations. We extract the prototype activa- tion vector 8 Enc(wo) and sample random noise 2r ~O I) to generate new images ((s, ar)) Fig. 24 presents one reconstructed image (using the same ZT) with three variations (using random .T). The results show that the majority of semantic information in the images is accurately recre- ated, confirming that the prototypes effectively capture meaningful semantic features.
Interpolation. Given two images xt and wy. we first retrieve its corresponding prototype activa- tion vector and starting noise by reverse DDIM process: (s* (s*,xt) and (s² ) and then generate new samples using (Lerp(s', :t), Slerp(at, 27; t)) for steps E [0, 1]. where Lerp/Slerp repre- sents linear/spherical linear interpolation respectively. Results are shown in Fig. 2
Visualization of the learned prototypes and consistency validation. Unlike other autoencoder- based diffusion models, our approach is explicitly designed to yield an interpretable semantic latent space, where each prototype is trained to capture distinct semantic content. In practice, we realize this by amplifying a prototype's activation and extracting the most responsive patch (see Sec. 3.3) Fig. B3 shows selected prototypes with their semantic visualizations, and Fig. 33p demonstrates that the most activated patches remain semantically consistent across samples.

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(a) Reconstruction
(b) Extrapolation
blonde eye make-up uike-pppp- carly hair
0.0
+.00
0.0
3.0
Input
Recon, (fixing s and xy)
Fixing varying
0.0
(c) Interpolation
Figure 2: Assessing the semantic meaning of the learned prototypes. (a) Reconstruction. (b) Extrapolation. (c) Interpolation. Left: CheXpert, from 75-year-old female w o enlarged heart (left) to 27-year-old male w/ enlarged heart (right), Right: 2 examples from CelebA
(a) Visualization of prototype semantics
(b) Consistency Validation
white collar curly hair eye make-up blonde
smile
wear red
X
X0
ju
white collar
consistent consistent
Ross
Vis. of
blonde
Figure 3: Prototype visualization and consistency. (a) Prototype visualization. Here, To de- notes the original image, a denotes the generated image guided by condition s', where s' the enhanced prototype activation on j-th prototype. Red square highlights the most activated patch, which considered as the visual representation of the chosen prototype, also shown in the third row. Note that prototype semantics are not pre-annotated but inferred through observation. (b) Vi- sualization across random samples demonstrates that each prototype encodes consistent semantics.
Manipulation and extrapolation. By adjusting the condition s we can edit the image with specific semantic requests. Furthermore, pushing selected dimension of to extreme values (ranging from 0.0 3.0) results in smooth and continuous enhancement of the associated semantic information, as demonstrated in Fig Unlike interpolation, which remains within the observed range, this process extends beyond the original data distribution, making it an extrapolation.
4.2 PROTOTYPE QUALITY
Prototype capability in semantic representation. We test the prototype quality via downstream classification task on the prototype activation vectors using a logistic regression classifier trained with 5-fold cross validation, reporting AUROC in Tab. & Our model outperform 3 out datasets in latent (prototype) quality, with particularly strong performance The lower latent quality for FMNIST may stem from Patronus prioritizing localized features, while DiffAE and InfoDiff emphasize global structures, which better capture the semantic information of
CelebA and FFHQ

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Published as a conference paper at ICLR 2026
Table I: Prototype quality and generation quality CelebA.
TAD1 1
Autrs 1
Latent AUROC1
FID
DifTAE InfoDiff
0.16=0.01
2.0+0.00
0.80+0.000
22.7--2.11
0.30: 0.01
3.010.00
0.84:00.00
23.6
w/ leamee
0.30 0.01
3.010.00
0.84100.0
22.3#112
Patronus
0.43
9.0+0.00
0.87:10.00
14.620.1
w/ learne
0.43
9.0+0..0
0.87:10.00
4.8+0.00
Table 2: Prototype quality and generation quality on FashionMMISTT, CIFAR-10, and FFHQ.
FMNIST
CIFAR-10
FFHQ
Latent AUROCT
FID
Latent AUROC
FID
Latent AUROCT f
FID L
DiffAR InfoDiff
0.84++0.00
8.2++.33
0.40+0.01
32,1+1.11
0.61++0.00
31.6-1.2
0.84:10.00
8.5.1.33
0.41
32.71112
0.61:10.00
31.2-116
w/ learned
0.84+0.000
7.400.2
0.41==0.00
31.5#188
0.61
30.9=2.5
Patronus
0.8240.00
14.71
0.54:10011
32.9 0.4
0.92:10.00
37.3=0.2
of learned
0.82.10.00
0.1
0.54
8.0
0.92.10.00
FMNIST due to its high inter-class variability. For the datasets where semantic information is more localized, Patronus achieves a marked improvement.
Prototype disentanglement. We quantify the disentanglement CelebA using TAD Yeatss al., 2022). Following Wang et al. (2023b), we first remove the highly correlated attributes, then compute the AUROC score for each dimension of the prototype activation vector An attribute is "captured" if any dimension achieves AUROC >> 0.75, TAD is the sum of AUROC differences between the top two predictive dimensions per captured attribute. As shown in Tab. Patronus outperforms previous models in both TAD and captured attributes.
4.3 GENERATION QUALITY
We assess generative quality using the Fréchet Inception Distance (FID), averaged over five random test sets of 10,000 images. Evaluation covers both unconditional and prototype-conditioeed genera- tion, where the latter incorporates learned prototype activation vectors from the test set (see Tab 2). Patronus significantly outperforms previous methods in pooooyye-coondttooee generation across all four datasets. In the unconditional setting, it achieves state-of-the-art performance on CelebA and remains competitive on others; noting that its effectiveness depends the training quality of both Patronus and the latent diffusion model.
4.4 DIAGNOSING SHORTCUT LEARNING IN DIFFUSION MODELS
We manipulated subset of CelebA to introduce an unwanted correlation between hair color and smile: all blonde/brown-haired images smile, while black-haired ones do not. Results confirm the model captures the introduced bias, as increasing the black hair prototype shifts the smile property from "smile" to "non-smile" and vice versa (see Fig. 4a). Similar patterns emerge without subset manipulation. In Fig. 2b, decreasing "eye makcup" reduces female features. This emphasizes how Patronus be utilized to discover unwanted model behavior, such as shorteut learning in this case.
DISCUSSION AND CONCLUSION
Prototypes Emerge at Different Times during Generation. Given generated image, Patronus reveals when each prototype emerges in the generation process by obtaining the prototype similarity score from estimated do at each timestep (more details in Appendix), as shown in the top-right corner of Fig. Tp. Thus, by subtracting the two time-sequential s of the semantically- image au, and Zg. illustrate how each prototype emerges temporally in the diffusion process. As shown in Fig. 4p. none of the prototypes have a significant emergence in the first 200 stages of generation. Interestingly, prototypes relating lower spatial frequency attributes appear earlier
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(a) Detect shortcut learning
(b) Trace prototype emergence
dark
Similarity Improvement
0.3
0.0
Hair color: light
1000
700
Timestep
100
Xa +black hair +blonde
X
+wear red +red face +smile
+ blonde emerges in later stage
+curly
less smile - more smile
emerges in earlier stage
Figure 4: Patronus
interpretable tool for (a) Detecting shorteut learning. Enhancing hair- color prototypes reveals their correlation with other attributes (e.g., smile), thereby exposing un- wanted biases. (b) Tracing prototype emergence. Different prototypes appear different stages of the diffusion process, as indicated by similarity score improvements, which provides insights for more effective image editing strategies.
during generation, such "wearing red";; while higher spatial frequency attributes, like "curly hair", emerge later in the generation process. This insight could potentially improve the efficiency image editing counterfactual generation guiding how far image should be reversed in the diffusion process. It could also support bias mitigation by leveraging the same mechanism once unwanted correlations are detected.
Prototype Correlation and Collapse. We test whether correlation between prototypes is caused by prototype collapse, where multiple prototypes represent the same semantics. To assess this, we introduce Prototype Distinct Loss to encourage prototype disentanglement and evaluate its impact compared to using the denoiser loss alone. The Prototype Distinct Loss defined as: Laisstint = 인/4 Σ max(0, 5 - minj Dig), where D is the cosine distance with absolute similar- ity: Dig = [p.: The margin is set to 0.5 and 1.0, where 5 = 1.0 enforces prototypes to be orthogonal. We initialize the model using the original network parameters and train it for additional 100 epochs using Ladpm + Ldistinnt- Results show that the new models do not see a sub- stantial change in the learned prototypes, suggesting that the prototypes optimized via the denoising objective are already sufficiently decorrelated without explicit regularization (see Appendix).
PcP;; P
Do We Capture All Relevant Attributes? While Patronus shows great ability in capturing tributes, we notice that global features, e.g. gender and age, are harder to find in one specific prototype. This could result from the patch-based prototypical encoder, making non-local features hard to capture. See Appendix for illustrative visual examples.
5.1 CONCLUSION
We propose Patronus, an interpretable diffusion model integrated with a prototypical network. enables intuitive interpretation of the generation process by visualizing learned prototypes (whar) and identifying where and when they appear. It also supports semantic manipulation through pro- totype activation vector. Experiments show that Patronus achieves competitive performance and learns meaningful prototype-based representations. We further explore its capability to diagnose unwanted correlations in the generative process. We believe Putronus offers valuable insights into interpretable diffusion models by bridging diffusion and prototypical networks.

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ACKNOWLEDGEMENTS.
Work on this project was partially funded by DTU Compute, the Technical University of Denmark; the Pioneer Centre for AI (DNRF grant P1); and the Novo Nordisk Foundation through the Center for Basic Machine Learning Research in Life Science (MLLS, grant NNF200CU062006,, The funding agencies had influence on the writing of the manuscript nor on the decision submit for publication.
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Patronus: INTERPRETABLE DIFFUSION MODELS WITH PROTOTYPES
Nina Weng, Aasa Feragen, Siavash Bigdeli Technical University of Denmark {ninwe, afhar, sarbifedtu.dk
Code is available at wmw.wwew..ghhhuuioooooouuuunuuad
APPENDIX
A
Additional Implementation Details
14
Interpretability:: Locating Most Activated Patches
14
A.2 Interpretability: Emergence Analysis
14
A.3 Hyper-parameterr for Training
15
A.4
CheXpert Dataset
16
Ablation Study
16
B.
I Number of Prototypes
16
Prototype Vector Size
18
Additional Experimental Results
18
Additional Visual Examples
18
C.2
Visual Representation of Prototypes
18
Captured Attributes by Single Prototype
18
C.4
Latent Quality for CheXpert Dataset
19
Prototype Consistency
24
D
Extended Discussion
25
D.I
Prototype Correlation and Collapse
25
D.2 Limitations of Cross-Modal Interpretability
25 ................................
D.3 Do We Capture All Relevant Attributes?
25
D.4 Justification for Excluding ProtoPNet's Additional Loss Terms
26
The Use of LLMs in this Work
26
Quantitative evaluation over interpretability
26
F1
Evaluating Prototype-Language Alignment via Image Captioning
26
Faithfulness measurement
29
Training Strategy
30
Robustness of prototype activation control
30
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A ADDITIONAL IMPLEMENTATION DETAILS
A.1 INTERPRETABILITY LOCATING MOST ACTIVATED PATCHES
Given an input image zo and chosen prototype the most activated patch determined as follows:
1. Compute the similarity score between each latent feature 2i, where i E (1.,.....N and the prototype p.. Identify the closest 2; as:
arg min PJ)
where (..-)) represents the chosen distance metric; in our case, it's the log transformed square L2 distance (Sec. B.I).
2. Map the index i' back the spatial coordinates of the feature map. Using the receptive field of the encoder, locate the corresponding patch in the original image, as shown in Fig. 5
A.2 INTERPRETABILITY. EMERGENCE ANALYSIS
Retrieve prototype activation vector along generation process for one image. For given guid- ance and starting noise 2T, can sample generated image zo, where 2, denotes the intermediate state at each timestep. At each timestep t, the estimated denoised image ái is computed as:
$6
(z+
v1 - āt (1(x,,,,)))
(8)
Vān
Here, the superscript & indicates that this estimate originates from timestep The prototype activa- tion vector at timestep t is then obtained as: st = Enc(āo,.). By repeating this process across all timesteps 1, we obtain the sequence {s") Extracting the j-th index rom each activation vector yields {s 1t=0 which tracks how the j-th prototype is activated throughout the generation process for sample To.
Examples of how prototypes emerge differently over time during diffusion. Building on the illustrative example in Fi where specific semantic features are enhanced or suppressed using prototype activation vectors, we examine how the five selected prototypes emerge over time during the generation process. This is shown for the original image (Fig(ah) with the enhancement of the prototype "White collar" (Fig(6p). and with the enhancement of the prototype "Curly hair" O)). Interestingly. when the "White collar" prototype is enhanced, its similarity score increases notably around timestep 700/1000 (Fig In contrast, when enhancing the "Curly hair" prototype, its similarity score begins rise around timestep 200/1000 (Fig(6)). Note that larger corresponds to an earlier stage of the diffusion process, which means "Curly hair" as a semantic information emerged later in diffusion generation process, compared to "White collar".
This observation further confirmed by the estimated denoised itj in Fig and Fig.e. White clothing appears early stage of the denoised images, whereas although the hair becomes fluffier, the fine-graing une-ggrieed curly texture only emerges around timestep 200 (bottom of Fig 6b). This difference
21
P2
64
Prototype extraction encoder
Pm
64
3
(xx)
Zn
Xo
Figure 5: Illustration how to find the most activated patch given zo regarding prototype J. Here the shape of xo (64 64 x 3) serves as an example and should be generalizable to other scenarios.
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(a) Original sample
(b) Add "white collar"
Stco 800
Step 930
Step 700
Step 6co
Step 520
p19 White collar
p67 Curly hair
p19
White collar
125
p82 up
p93 Bllndd hair
125
p53 Blonde hair
Eye up
Smill
1.00
p55 Smile
075
0.0
030
0.25
0.55
0.00
1000
900
800
700
600
300
100
300
200
100
1000
500
000
700
600
500 Eimestp
100
300
200
100
liTTNsi MTD
Step
Stap 400
Slep 200
Stup 203
Stap 100
Stup
(c) Add "curly hair"
(d) Difference in prototype activations over time
p.p: White collar
ps7: Curly har
3.50
White
0.3
2.00
p95
Siiterty
0.75
01
0..0
1000
20C
8000
200
500
500
420
200
200
300
Grnoscpp
01
1000
900
800
700
600
500
400
300
200
100
timestep
Stop 400
Stop 300
Stop 200
Stop 100
Stop
Figure 6: Prototype emergence details in: (a) the original generated sample; (b) generated sample with "white collar' added; (c) generated sample with "curly hair" added; (d) differences in prototype activations over time. Sharp increases indicate the timesteps when prototypes begin to emerge prominently in the enhanced sample compared to the original.
summarized in Fig 6d. where we show the difference in prototype activation vectors between the enhanced and original samples. The sharp increases in the plotted curves indicate the timesteps which the respective prototypes begin to emerge prominently.
Trend analysis on a larger sample set. To analyze the trend more comprehensively, randomly select 100 samples from the test set and enhance all 100 prototypes for each sample. We then compute the average difference between the enhanced activation scores and their original values, visualizing the results as an averaged curve, just as shown in Fig.
A.3 HYPEERPARAMETERS FOR TRAINING
More
for Patronus training could be found in Table We trained all experiments with Adam optimizer, learning rate of 1c
For the latent diffusion model, we trained with an ID UNet, with base channels of 64, and channel multipliers (1,2,4). We set the dropout rate as 0.2 and learning rate 1e-4
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Table 3: Hyperparameters used for Patronus training.
Input size
Num. p
shape of
patch size
Num. patches
Nom. Channels
Num. Channel.
FashionMNIST
(1,32.32)
30
(1.1.188)
14x
1,2,4,4
Cifar10
(3,32.32)
100
(1,1,128)
14x
100
64
1,2,4,4
FFHQ
(3,64,64)
100
(1.1.128)
14x
672
1,2,4,4
CelebA
(3,64,64)
100
(1,1,128)
14x14
672
64
1,2,4,4
CheXpert
(1,224,224)
(1.1,188)
1,2,4,4
Table 4: Ablation study on CelebA, regarding the number of prototypes and the prototype vector size.
# #p
shape of p
TAD f
Attrs f
Latent AUROC
FID +
0,8291 = 0. 0.0146
9.0000 0.0000
0.8288 + 0.0022
6,0126 0.0740
0.8515 0.0545
9.2000 0.4000
0.8527 + 0.0017
6.5395
0.0676
(1,1,128)
100
0.5395 0.1205
12.0000 + 1.0954
0.8646 + 0.0010
5.4871
0.0151
128
0.4700 + 0.0758
12.0000
0.8944
0.8713 1 0.0018
5.1264 0.0488
(1,1,64)
0.5860 = 0.0535
8,4000
U.4899
0.8491 + 0.0021
5.2017
0429
(1,1,128)*
0.8515 0.0545
9,2000 0,4000
0.8527 1 0.0017
6.5395
0.0676
(1,1,256)
0.4779 0.0045
8.0000 0.0000
0.8492 0.0008
6.2918 0.1279
The result is the same as the second row since the hyper-parameters are identical. We listed as another row for easier comparison.
For prototype encoder, in this work apply 4-layer convolutional network with ReLU activations. The channel progression - 32 - - - 128. All convolutional layers use 3 3 kernel, with strides of [2, 1, 1, for the four layers and paddings of [1, 0, 0, 0], respectively.
A.4 CHEXPERT DATASET
In this work, we use a subset of the CheXpert dataset (Irvin et al. 2019). retaining only frontal chest X-ray scans. To mitigate potential information leakage and reduce memorization effects due to matient-specifer variations, we sample single scan per patien This preprocessing step yields total of 28.878 chest X-rays, of which 90% allocated for training and the remaining 10% for testing.
B ABLATION STUDY
As shown in Tab. 4 present ablation study of Patronus with respect to the number of proto- types and the dimensionality the prototype vectors. Experiments are conducted on the CelebA dataset using input resolution of (3, 64, 64), training duration of 200 epochs, and learning rate of 1e-4. Note that FID is computed in the context of conditional generation.
B.1 NUMBER OF PROTOTYPES
We evaluate the impact of varying the number of prototypes, setting #p = (32, 64, 100, 128), while fixing the prototype vector size to (1, 1, 128). As the number of prototypes increases, we observe consistent improvements in latent quality (measured by AUROC), the number of attributes captured, and the FID score. However, the TAD score-which reflects the disentanglement quality-tends to decline. This trade-off is expected: while a larger prototype pool allows the model to capture more fine-graine ine-grained visual patterns, it also introduces redundancy, reducing the distinctiveness and interpretability of individual prototypes. This suggests the existence of an optimal prototype budget for balancing generation quality and disentanglement.
Thhe number of recordings per patient in CheXpert is highly imbalanced, ranging from I to 89 Weng et al. 2023). Notably, disease severity is correlated with scan frequency feweer fewer than 25% of control subjects have than five scans, while this proportion exceeds 50% among patients.
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Input
Recon.
Input
Recon.
(fixing s and xn)
Fixing 5, varying Xy
(fixing s and Xy)
Fixing s, varying Ky
(a) CelebA
(b) FMNIST
Input
Recon.
Input
(fixing s and Xy)
Fixing s, varying Xy
(fixing and xy)
Fixing s, varying Xy
(c) CIFAR-10
(d) FFHQ
Figure 7: Additional examples of reconstruction and variations with fixed and random TT.
(75-year-old. Female, w/o discuse)
(27-year-old. Male, discase)
(43-year-old, Female, w/o discase)
(21-year-old, Male, disease)
(89-vear-old, Male, discase)
(36-year-old, Fermale, w/o disease)
(56-year-old, Female, disease)
(S3-year-uld, Male, w/o discase)
Figure 8: Additional Image Interpolation Examples on CheXpert. Interpolations are shown
between two real chest X-rays, with patient age, gender, and disease condition (Cardiomegaly).
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B.2 PROTOTYPE VECTOR SIZE
A larger prototype vector size allows each prototype to encode more detailed semantic information within fixed spatial region. Conversely, when the prototype vector size is small, the model may struggle to capture sufficient semantic richness. However, excessively large prototype vectors may introduce optimization challenges, such as slower convergence and increased redundancy. This trade-off is reflected in the results shown in Tab. where prototype vector size of (1, 1, 128) yields the best overall performance.
C ADDITIONAL EXPERIMENTAL RESULTS
C.I ADDITIONAL VISUAL EXAMPLES
Reconstruction and Variation with Random JT We provide additional visual examples across multiple datasets in Fig. 7 These results demonstrate Patronus' ability to capture semantic features across different datasets.
Notably, while Patronus effectively preserves fine details and patterns, struggles with rare patterns. For instance, in the third row of Fig 7b the model fails to reconstruct the Adidas logo accurately. Another interesting case appears in the last row of Fig 7 where Patronus successfully identifies the presence of hat but generates variations of different hat styles instead of exact reproduction.
Interpolation We provide additional visual examples of interpolation across various datasets, in- cluding CheXpert ()i as well as FMNIST, CIFAR- 10, CelebA, and FFHQ (Fig(9)) In datasets with well-defined class clusters, such as Fashion-MNIST and CIFAR-10, 10, interpolation tends to be less effective when transitioning between images belonging to different classes (Fig. 9b).
More Visual Samples for Diagnosis Ability of Patronus In Fig 10 we present additional samples and hair-color-related prototypes for the diagnosis task, revealing a more pronounced bias bias-enhanciig hair color also affects the presence of a smile. More specifically, we showcase eight cases, including four female and four male subjects. Within each gender group. we include two individuals with black hair and smile, alongside and a smile. When enhancing black hair-related prototypes, all images transition to a non-smiling expression (as seen in the fourth and sixth columns of IOp.
individual with brown blonde hair
C.2 VISUAL REPRESENTATION
PROTOTYPES
We present complete visual representation of the learned prototypes from the CelebA dataset in Fig. [T [2] Below each prototype, we provide summary of semantic meaning based on human observation without explicit annotation. Consequently, these interpretations may contain inaccuracies. For prototypes where a clear semantic meaning could not be determined, we leave the description blank. Notably, these blank descriptions highlight the inherent limitations of language in capturing visual concepts. The visualization process follows the steps outlined in Sec, 3.3
C.3 CAPTURED ATTRIBUTES BY A SINGLE PROTOTYPE
As shown in Tab. D we applied TAD and the number of attributes captured estimate the proto- type disentanglement ability, where Patronus remarkably outperformed the SOTA by nine captured attributes, while prior methods capture most three. Tab, details which prototypes capture these attributes, with visualizations in Fig. 13
As shown in Tab. 5 single prototype can capture multiple attributes. E.g. prototype 82 cap- tures both "Eyeglasses" and "Rosy_ "Rosy_Cheeks" _Cheeks".. This finding is particularly interesting, prior visual inspection suggested that prototype 82 represents the concept of "Heavy Eye Make-up" (Fig. 3 which is semantically related to both attributes: heavy eye make-up often co-occurs with rosy cheeks and tends to be negatively correlated with eyeglasses, possibly because individuals wearing heavy makeup more likely intervention results shown in Fig. [3] where suppressing the activation of Psz leads to the appearance of eyeglasses, while enhancing the activation induces both rosy cheeks and prominent eye make-up.
contact lenses instead. This observation is further supported by the
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(a) same-class.
(b) Across classes.
Figure 9: Additional interpolation examples. (a) Interpolation across datasets: Fashion-MNIST (first two rows), CIFAR-10 (third and fourth), CelebA (fifth and sixth). FFHQ (seventh and eighth) (b) Cross-class interpolation MNNSS, CIFAR-10) showing incoherent transitions.
Table 5: Captured Attributes in CelebA.
Captured Attributes
Captured Attributes
Captured Prototype Index
AUROC
Bald
0.8276 + 0.0041
30
Bangs
0.8411 + 0.0022
Black_Hain
0.8104 + 0.0034
Blond
0.8917 + 0.0022
93
Blurry
0.8717 + 0.0058
35
Eyeglasses
0.7967 + 0.0026
82
Pale Skin
0.8549 + 0.0044
Rosy Cheeks
0.8226 0.0040
Wearing_
0.9002 1 0.0025
C.4 LATENT QUALITY FOR CHEXPERT DATASET
We analyze the latent quality for CheXpert dataset by measuring TAD, number of attributes being captured and latent AUROC in Tab. total 23 attributes are evaluated, comprising four demo- graphic attributes, four indicators related to patients' socioeconomic or health status, shortcut
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Input Pz1(-0.01/0.84)
P35 (0.07/0.79)
P13 (0.18/0.71)
(0.19/077)))
P72 (0.14/0.85)
Pao (0.19000
Black related
Brown hair related p
Blonde hair related
Figure 10: More visual samples and more hair-color related prototypes for diagnosing un- wanted correlations with Patronus. By selecting the top two hair color prototypes (highest AU- ROC for single- single-dimension prediction). their correlation with "smile" prototype reveals dataset bias. Py (a/b) define as: (a) Spearman correlation with the "smile" prototype (green: positive, red: nega- tive), and (b) AUROC for predicting hair color when using this prototype (bold if > 0.75).
feature pacemaker (annotated by Weng al. (2024). ) and 14 disease-related labels. All attributes are binarized as detailed below Bl Age (260 or <60), Sex (Male or Female), Race (White or Non- white), Ethnicity (Hispanic/Latiee Latino Other), Insurance (Enrolled in Medicare Not), Interpreter Need (Yes or No), Deceased (Yes or No), and BMI (within the normal range: 18.5-25.0, or outside).
Evaluating latent quality. As shown in Tab. 6 the learned latent representations demonstrate strong predictive capabilities for most demographic attributes, such as age, sex and BMI, achieving high AUROC scores using simple logistic regression model. Notably, the latent space also en- codes information relevant to the presence of a pacemaker. Furthermore, it supports the prediction of several cardiopulmonary conditions-such as Cardiomegaly, Edema, and Pleural Efflusonnwwin AUROC values exceeding 0.75 (bold font in the table). These results indicate that the latent repre- sentations capture semantically meaningful and clinically relevant information. For comparison, include the performance a ResNer-50 baseline in the Tab, 6 It is worth noting that this baseline was trained on 320x320 resolution images (Bressem et al. 2020), and that the original CheXpert validation set includes only samples from five disease categories. As a result, performance metrics for the remaining categories unavailable.
Interpreting Captured Attributes and Their Corresponding Prototypes. Among the 23 at- tributes, three are captured by single prototype, as listed in Tab, 6 with visualizations provided in Fig. 14 Notably, the Sex attribute is captured by a prototype that focuses the edge of the chest wall (Fig. 14a). In the extrapolation experiment on prototype p1 (Fig.[14) we observe a decrease in rib cage size as the prototype similarity score increases. This observation aligns with clinical find-
We acknowledge that the binarization is not ideal, it may be white-centralized and introduce bias; how- ever, is adopted here for the sake of simplification.
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Pot Green
Pi: Blue
Ps: Rid/Pi
Pai
Textin partern
Gmy BG
Right- chin
Pyd
Pai Waer green
No -hair
BG
om
Pao:
Past-
Piz: <ide face
Pa.: tumnnng left
Pus hair with neck
Pist ron
Pas: Purpl
Pari
Pag:
Piol collar
uail DG
BG
k edge
Celeb A artiffect
Pas: Grey
Poz
Post the right
Yello wi highlight
Past Wea yellow
Paud
Pay: Left-side fice w/ hair
Pest Shado face
Pay Yell ower faee
top
Paod hair
Past Bald
Pagi Right- side hair
Papi
Pasi ge BG
Pasi-
Pao: face
Papi
Pay face
Pay Carl
side forchead
hair
Pas: Grey BG
Pal-
Pat-
Faz Yellow
Posl Small er eyes
Past
Fusi Ligh bblue
Par
Pap
fioe
hair
clothing
Figure 11: Visualization of learned prototypes on CelebA (prototypes 0 - 49). Each row shows 10 prototypes with three views per prototype (top bottom): original image, image enhanced with prototype and most activated patch (serves as the prototype visualization). BG means background.
ings that females tend to have disproportionately smaller rib cage compared males (Bellemare al. 2003) 2001). For the Age attribute, the corresponding prototype is most activated in the region around the upper thoracie vertebrae (Fig. 14a). This may relate the age-associated ossification
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AGE
Pist
Play:
Plaar
Plat
Pisct Grey hair
Post Crey flurry
Piss
Pert long hair
Psol fiiee
Pist Black
go BG
ge hair
blue
pink
BG
Pan: Big
Pas: Blonde stmight hair
Pas: Yell ower fhee
Pest Brown straight hair
Plast
Past
Pac: QR code partem
Pust Cu hair
Pus: Caleb A antifctts
Pon: Long brown hair
ger chin
hair
Prot
Prat blue
Pozl blue
Pryt hair top
Phot
Fyg! dark
Phot Light skin
Payt
Pry top black hair
Pryi
Plas
Paz: Heavy eye make-up
Celeb artifacts
Posi Beige
Pes: Black
Horizo
Pau: Wear green
Pay: Red
Pop: Squin
Pay: BG
Pay: to leit
Pog: de hair
Pug: black behind
Pagi
Psg: hair
Pyyt
Pagi Pay: chin
ting
Smile
rud
kbones
Figure 12: Visualization of learned prototypes on CelebA (prototypes 50 99). Each row shows 10 prototypes with three views per prototype (top to bottom): original image, image enhanced with prototype j. and most activated patch (serves the prototype visualization). BG means background.
of the costochondral cartilage of the first rib McCormick & Stewart 1988 Radiology Masterclass, n.d.). Regarding the Pacemaker attribute, the most activated patch is located near the upper region of the heart, potentially reflecting the correlation between pacemaker presence and underlying car-
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"Bangs"/ /
"Wearing Hat"
0.0
3.0
p50
"Bald"
535
"Blurry"
0.0
3.0
PPale Skin"
D78
"Black Hair"
0.0
3.0
"Fyeglasses"
paz
Cheeks
0.0
3.0
BBlond Hair"
593
0.0
Figure 13: Visualization of Captured Attributes in CelebA via Explorations. The text on the left indicates the attributes captured in the CelebA dataset. Note that a single prototype can potentially capture multiple attributes.
fas'
Vis. of
Plu
P1
P1
0.0
3.0
P54
P54
3.0
(a) Visualization of prototypes captur- ing attributes.
(b) Extrapolation over the same prototypes.
Figure 14: Visualization and extrapolation prototypes capturing attributes from CheXpert.
Three attributes are captured: Age (by P3s), Sex (by pı). and presence of pacemaker (by P34).
diac conditions. In Fig. 14b increasing the activation of prototype Psa leads to more pronounced appearance of a pacemaker.
We acknowledge the limitations of our medical expertise and do not intend to draw definitive clinical conclusions from these observations. We welcome researchers with medical backgrounds to further evaluate and interpret these findings.
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Table Latent Quality of the CheXpert Dataset. The latent AUROC denotes the average AUROC across all considered attributes. A total of 23 attributes are evaluated, comprising four demographic attributes, four indicators related patients' socioeconomic or health status, one shortcut feature (pacemaker), and 14 disease-related labels. All attributes are binarized. Bolded values indicate AUROC 2 0.75.
Demugraphic
Shurtcut
AUROCT
Age
Sex
Race
Diniiiiy
Insurance
Interpecter
Decessed
BMI
PM
0.12:001
30:0.0
0.74:0.01
0.88-0001
0.98
0. 0.70=0.01
0.724001
0.74+0.00
0. 0.74-000
0660000
0.77=0.01
0.92:0.01
Discase Labels
Finding
No
Candio
megaly
Opacity
Lesion
Edeaa
Cossoli-
Pneumo-
Elfusion
Pleural
Pleural
Fastuee
Support
0.56
0.90
0.80
Captured Attributes
Captured Attributes
Captured Prototype Index
AUROC
Age Group
0.7891 = 0.0031
38
Sex
0.8806 0.0005
1
PM
0.7823 1 0.0028
34
AUROC Performance from Latent Representations. The reported AUROC values reflect the performance of logistic regression classifier trained the latent representations using 5-fold cross-validation. Demo- graphic and other attributes are binarized, details are in text.
Pacemaker, annotations from Weng et al. (2024).
Enlarged Cardiomediastinum
C.5 PROTOTYPE CONSISTENCY
Prototype visualization consistency for one run. We quantitatively evaluated the consistency of prototype visualizations in one run. As the visual concepts are very small patches, consistency is measured directly in pixel space, which faithfully captures their low-level structural differences.
Using Euclidean (L2) distance between visualization pairs, we observe a clear separation: within- prototype 2.31 between- perween-roootype- 3.95. A two-sample t-test confirms this separation is highly significant (t= 19.55, p 1.6 10 (7)3), This confirms that each prototype forms a coherent and distinguishable visual concept.
Learned prototype consistency cross different run. This part evaluates the consistency of learned prototypes across different random initializations. We'd like to emphasis that goal is not to enforce identical explanations across models, but to explain each model's behaviour as it is trained. If models use different internal concepts during generation, then different prototypes are expected and even desirable. Nevertheless, under almost identical training conditions (same architecture, dataset, and objective), is still meaningful to assess whether the learned prototypes remain consistent.
To so, we compare prototype activation patterns on the same input batches rather than directly comparing prototype vectors, since the latter are not aligned across runs due to arbitrary rotations the feature space. For each batch B images (here B 512), we obtain prototype activations from both models:
A A NxB
where each row corresponds to the activation pattern of one prototype across the batch.
We then compute the pairwise cosine similarity matrix:
(A), A3)
S = ||A||| ||4311
To resolve the permutation ambiguity between prototype indices, we determine the optimal one-to- one alignment using the Hungarian algorithm
Σ Si,(())-
= arg max
|-| mapping
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original margin=0.5 margin=1.0
Density
2-
original
Threshold
Margin 0.5
Pearson correlation distribution
Margin -1.0
(a) Distribution Pearson correlation values for prototype similarity scores, computed from 1,024 randomly selected training samples. Results compare the original training and training with additional loss term. The difference is minimal, with only small fraction of pairs exceeding an absolute corre- lation of 0.5 (red dashed lines) for all cases.
White collar
Red
Curly hair
Blondo
Smile
Wear
face
red
Prototype visualizations and their semantic inter- pretations remain consistent regardless of the addi- tional loss.
Figure 15: Effect of additional loss on prototype diversity. (Top) Pearson correlation distribution of prototype similarity scores shows minimal differences regardless of additional loss. (Bottom) Prototype visualizations remain consistent, indicating negligible impact on semantic representations.
We then assess how consistently this alignment appears across all test batches. The resulting permu- tation consistency score is 0.882, indicating that the prototype-to trottype-o--rooootpe largely stable, with most prototypes consistently aligned between runs. After applying the optimal alignment, the matched prototypes achieve extremely high similarity (mean R 0.99), indicating that the semantic behaviour of prototypes is almost identical across seeds.
prototype mapping across seeds is
D EXTENDED DISCUSSION
D.1 PROTOTYPE CORRELATION AND COLLAPSE
As discussed in Sec. we further provide additional experimental results in Fig. These results confirm that the new models do not show substantial changes in the learned prototypes, suggesting that prototypes optimized via the denoising objective are already sufficiently decorrelated without explicit regularization.
D.2 LIMITATIONS OF CROSS-MOuT MODAL INTERPRETABILITY
We emphasize interpreting diffusion models without cross-modality by design for two key reasons:
Language's limitation in representing complexity: While language, discursive symbolism, serves as a powerful medium for interpretation, alone cannot fully represent non-symbolic sensory and semanties complexity (Langer] 2009). This is evident in the superior performance of multi- modal learning over single-modality; prior work (Gial et al.) 2022: Goyal et al. 2017) further shows the restricted perceptual capacity of language-only generation by introducing visual cues.
Inherited bias from the text embedding: (1) Incomplete text representations: if certain concepts explicitly named (e.g., medical devices in radiology reports), the model cannot learn their visual counterparts. (2) Spurious correlations: text data may encode unintended biases, such as differing report detail levels by patient demographics, which may propagate into generated images.
D.3 Do WE CAPTURE ALL RELEVANT ATTRIBUTES?
We further illustrate the challenge of capturing global features with sample results Fig. 16 Fol- lowing the experiment described in Sec. 4.1 reconstruct images and their variations using fixed s with either fixed or random Fr. While fine-grained details, such as cheekbone structure the sec- ond row and shirt details in the first row, well preserved. the generated images with varying Fr fail to capture age or gender consistently on harder scenarios. For instance, the middle image in the first appears noticeably younger than the original, while most variations in the second row
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Input
Recon. (fixing s and Xy)
Fixing varying Xy
Figure 16: Patronus may struggle to capture global features such as age and gender. To illustrate this, we present three samples generated using the fixed s with random IT. While the generated images successfully preserve semantic attributes such as hair color, checkbones, smile, shirt, and background color, they fail to accurately reconstruct age or gender.
depict more mature appearance. Additionally, in the third sample, gender shift from female to male is observed, possibly influenced by the presence short hair.
D.4 JUSTIFICATION FOR EXCLUDING PROTOPNET's ADDITIONAL LOss TERMS
In this work, we optimize the model using only the denoiser loss. A key question arises: Is this loss sufficient? Besides the attempt in Sec. (Fig. [5) to add an extra disentanglement loss, we further compare with ProtoPNet. which, alongside cross-entropy loss for classification, introduces two additional loss terms: (1) Cluster loss (Clst): encourages each training sample to have at least one patch close to prototype: (2) Separation loss (Sep): pushes latent patches away from prototypes of other classes.
Neither loss applies in setting: (i) prototype representation is supposed capture the underlying data distribution, not to enforce proximity to specific training samples; (ii) our method is generative and does not rely class labels, making class class-dependent separation constraints irrelevant. Thus, the denoiser loss is sufficient, as ProtoPNet's additional terms do not align with the objectives.
E THE USE OF LLMs THIS WORK
In this work, we used large language models (LLMs) solely to assist with the presentation of the paper, including grammar correction, wording refinement, and minor sentence shortening (at the level of one or two words, not entire paragraphs). LLMs were not used for any other purpose. We always check the content after using LLMs, and we responsible for the content that we submit.
F QUANTITATIVE EVALUATION OVER INTERPRETABILITY
F.I EVALUATING PROTOTYPE-LANGUU ALIGNMENT VIA IMAGE CAPTIONING
An additional experiment was conducted to examine whether language- based analysis can support the alignment between visual concepts and human understanding. Specifically, we aim to assess whether our prototypes encode visual features that are both visible and interpretable through human concepts. To this end, we used the BLIP model gingFace) to caption the prototype-ee prototype-enhanced enhannedd image for prototype j and the corresponding original image, and then compared the words with the most significant increases. For example, for the proto- type corresponding to 'curly hair' in the visualization (Fig. 3a), the top three words with the highest increases after applying the prototype are 'curly': 409, 'hair': 307, 'long": 66, where each value
on Hug-
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P.Z
Backa11a
Homani114
comanasa
Bngiio
Boadi57
Backs102
passesisa
Pearingiss
blond
oonan101
Bassess22
boldingies
painss
bainas
Boadida
hainso
Bairis
Rackiss
plassesues
plackua
passesiaa
fatingia7
imilingi22
bainsa
2.
16
Top Counts
Bolding:s
Backs2
holdngg:63
hair157
oomanes
suryyaos
Womand1
Fol:71
piress
Backso
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pnone:36
Bnga229
Bairs7
hairaao
Bnnck:s3
Phone771
hainsa
Mossessss
blondi52
bella4
preenise
longis
longids
hotsings
P.20
P.22
p.25
P.24
p.25
2.26
p.27
p.28
p.29
plasseedss
millingtaa
332
223
backka2
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homenot1
Womans1e2
Bucksso
preen774
Jackss
pommaods
We 199
Homaniza
fress:s7
bains
headiss
Backsa
bainse
Junaia
grosses:s1
Counts
p.30
P.31
p.32
p.3s
P.54
P.35
p.36
p.37
p.38
P.39
holdings42
33
Nahan:253
blond:a7
Heasa
Black:91
longaac
Brangec174
fontisa
gress1007
holding:30
Bowe1
baicza
hand:39
1oman:124
hoiding:76
Basses:62
homan:33
Bum771
hand:3
Biohde2266
Jtooses:ss
Visualization of learned prototypes on CelebA with the top three increased words using BLIP for captioning (p0-p39). Each row shows 10 prototypes with three views per prototype (top to bottom): original image, image enhanced with prototype j, and most activated patch (serves as the prototype visualization), and the top three increased words.
Figure 17:
reflects the difference in word frequency between the enhanced and original images across 672 sam- plcs. We provide the full list of the top three increased words, along with prototype visualizations from one run, in Fig. 17 18 Note that this evaluation depends strongly on the captioning model's
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Top Counts
Bomanc212
greent2es
phitu:77
air
man:74
greano7a1 31
hamances
blckkaz
plasses:53
Preesc79
Barr132
handingsso
oomanca
bteck:77
plassenc7s
ahim:31
blond:33
haldingsss
10rt
blond:59
bottngs22
batrss
head:
595
p.51
p.52
9.53
P.54
p.55
p.56
p.57
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0.59
Baack177
ackaba4
munsies
oomencios
bluszar2
Bade1so
hainaa
geens132
Bead:108
2chtt:209
glesssace3
Buckes
Bue:202
Bodding:22
Jaceet:5p
ong:54
Ind
Top
Btond:74
black:42
iniez7
Bueci2o
hire:102
Jong:35
homan:30
loggsoo
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Bair:300
Sinssec:52
Back:111
hair:5a
hainda
Bhesect377
8e:70
nimss
c007
woman:ss
Weset:15o
contioo
Jong:42
lngasa
airt:s
plaeses:00
207
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headi51
Bes112
Slassessps
blassessa7
faces22
Top Counts
7.71
72
P.73
P.74
P.75
P.76
p.27
P.78
P.
Shiteias
Shiras
hiieess
ainss
Backk7
plassesi477
cchet1114
plassesisa
Bhiris
Mondi50
blackis
mam197
holdingnss
blasses:31
white:s4
bassessaa
Stting:38
bead:59
Bplo13s
blond44
Shimcs
Bonndzs
Figure 18: Visualization of learned prototypes on CelebA with the top three increased words using BLIP for captioning (p40-p79). Each row shows 10 prototypes with three views per proto- type (top to bottom): original image, image enhanced with prototype j. and most activated patch (serves as the prototype visualization), and the top three increased words.
vocabulary, For instance, prototype 34 in this run corresponds to a heavy eye-makeup enhancement, but the model fails describe it and instead produces only gender-related terms.
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0
Counts
longr355
homands3
purplerr3o
230
Blackr132
$tirt:70
oomani115
valow:332
hansios
gasssesia
umiling:
hirtzeee
Hues10
plassesuce
buues7
2idings220
romas
manr77
Scartis
ongc1os
blond:se
bairs?
holdingoss
1onamas117
ao
shiessa
8.88
Homanis
Momani 142
plassesi44
807
Stirtisa
R01107
Wbn1177
ronnss
Nomam133
smilllg:411
bairi6
black30
pink 339
Jackss
bakeeiz4
hiims
biccuse
Bondis
peitessa
back:50
Jong:as
hain29
Bcket1135
bat:35
Back71
beara:41
Srandings44
Bacck7
piitis
Figure 19: Visualization of learned prototypes on CelebA with the top three increased words using BLIP for captioning (p80-p99). Each row shows 10 prototypes with three views per proto- type (top bottom): original image, image enhanced with prototype j, and most activated patch (serves as the prototype visualization), and the top three increased words.
Comparison of P-Score Increase for i= vsv i
P-Score Increase on Pj
0.6
0.4
0.0
-0.2
Enhance on p:
Figure 20: Faithfulness evaluation of prototype scores. Enhancing prototype p; leads to clear increase in its own (i = j). while scores of other prototypes remain near zero (i A j), indicat- ing that the similarity measure behaves as intended.
F.2 FAITHFULNESS MEASUREMENT
To evaluate the faithfulness of the prototype scores, measure whether the score of prototype P: increases when the corresponding condition enhanced. For each prototype pi, we generate an enhanced image using p; (x-axis) and compute the change in all prototype scores (y-axis). The results are shown in Fig. 20 The target prototype shows a clear increase (mean around 0.35), whereas the remaining prototypes remain zero, indicating that the similarity score functions as expected. The two observed outliers correspond to prototypes that encode minimal semantic information based their visualizations.
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X
Xf
Conditional DDPM
Image
Forward diffusion
loss
Prototype module
Encoder
Prototype
Similarity
vectors
score
Figure 21:
Training Overview. The black arrows show the forward computation: the encoder extracts prototypes and obtains similarity scores, which condition the DDPM during generation. The blue arrows indicate the backward path of the denoising loss, which updates both the prototype module and the conditional DDPM jointly.
G TRAINING STRATEGY
The training strategy for Patronus direct and simple. All components are jointly trained with solo training objectives, i.e. the denoiser loss. Fig. [1 shows the training overview with both forward computation and backward loss propagation in black and blue arrow respectively.
H ROBUSTNESS OF PROTOTYPE ACTIVATION CONTROI.
We evaluate the robustness of prototype activation control by comparing the cosine similarity be- tween clean images and images generated with noise- perturred prototype activations. We add ran- dom noise with magnitudes [0.1, 0.5, 1.0, 2.0, 5.0, 10.0]% of the maximum activation value (fixed to 2.0 in our experiments). mportantly, we perturb all prototype activations, not just single prototype. Image similarity measured using the cosine similarity Inception V3 embeddings.
Fig. 22 shows the results, clearly illustrating that the prototype activation score function is highly robust, that the cosine similarity decreases slightly low noise levels. When the noise magnitude increase to 10%, the embedding similarity becomes comparable to that between random clean image pairs. Visual examples generated images with noise added to the activation scores are provided in Fig. 23
Cosine Similarity
0.9
0.8
Random Clean Image Pair Mean
Random Clean Image Pair Std
0.6
Embedding Similarity(Noisy, Clean)
0.10%
0.50%
1.00%
2.00%
5.00%
10.00%
Noise Magnitude
Figure 22: Prototype activation control robustness quantified by comparing the embedding simi- larity between clean images and images generated with added activation noise.
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Clean Img
Noise: 0.001
Noise: 0.005
Noise: 0.01
Nolse: 0.02
Noise: 0.05
Noise: 0.1
Img
Noise:
Noise:
Figure 23: Visual examples of generated images after noise is added to the activation scores.
31


Reviews:
[{"review_id": "UVTAW7CrUp", "reviewer": "Reviewer_JUEx", "summary": "This paper introduces Patronus, a diffusion model that incorporates prototypes to provide more interpretable generation. The model learns patch-level prototypes and uses prototype activation vectors to condition the diffusion process. The authors claim that this enables understanding of what semantic patterns emerge, and where and when they appear during denoising. The paper also proposes a sampling-based prototype visualization strategy. Experiments on several datasets are presented, focusing on semantics, reconstruction, guidance, and bias analysis.\nThe goal of embedding interpretability into diffusion models is timely and important. The direction of prototype-based interpretability for diffusion is interesting.", "questions": "1. What exactly is the architecture of the prototype encoder? Is it a ResNet? How many layers? Is it trained jointly or frozen after pretraining?\n2. Are prototypes randomly initialized and learned, or do you initialize from real patches?\n3. How is the reverse DDIM performed in practice? Please provide pseudocode or a precise description.\n4. Try to be more clear, direct, and less cryptic in the explanation.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "* Tackles an important problem: interpretability in diffusion models.\n* Clear motivation for moving toward intrinsic interpretability rather than post-hoc analysis.\n* Prototype idea has conceptual appeal and builds on a known line of interpretable ML research.\n* Good results in certain controlled settings with simple datasets.", "weaknesses": "* **Clarity issues:** The paper is written in a cryptic way and often hides a simple idea behind heavy verbal descriptions. The prototype module is not sufficiently described at the engineering and procedural level. For example, it remains unclear whether the prototype encoder is a standard CNN, how it is trained relative to the denoiser, and whether prototypes are learned or chosen a priori. Section 3.1 in particular does not provide essential clarity on the prototype encoder architecture and training scheme.\n* **Name inconsistency:** This is actually a minor comment, but the acronym “Prototype-Assisted TRANsparent diffuSion model” does not actually map to “Patronus”.\n* **Proposition lacks rigor:** The stated proposition is informal and the proof is neither formal nor convincing. There is no precise mathematical statement or measurable criterion. The argument reads as a loose justification rather than a proof. It should be rewritten to be mathematically precise or removed.\n* **Reverse DDIM reference is vague:** The paper refers to a “reverse DDIM process” (line 313) but never clearly defines how it is implemented.\n* **Weak empirical depth:** Experiments rely mainly on small and simple datasets (FMNIST, CIFAR-10, CelebA subsets). This limits confidence in claims about semantic interpretability and scalability. Higher-resolution and more complex datasets (ImageNet, large natural scenes) would significantly strengthen the case.\n* **Interpretability not fully grounded:** The prototype activations are visually inspected and interpreted manually. There is no systematic evaluation of interpretability quality, faithfulness, or prototype consistency beyond selected qualitative examples.\n* **Simple baseline missing:** A naive “mask-based or patch-embedding supervision” or a direct attention-map visualization baseline would help demonstrate that prototypes add more than a different form of patch scoring.", "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": "yfAEJ4uZZa", "reviewer": "Reviewer_WfoU", "summary": "This paper introduces Patronus, a diffusion model that integrates a prototypical network within the denoising architecture to provide intrinsic interpretability.\nInstead of relying on post-hoc feature visualization or external semantic encoders, Patronus learns patch-based prototypes that encode localized semantic patterns (''what\"), their spatial location (\"where\"), and temporal emergence during denoising (\"when\").\nPrototype activation vectors condition the diffusion process, allowing direct visualization, manipulation, and bias diagnosis (i.e., shortcut learning).\nEmpirical results across five datasets:\n- Achieves competitive or superior FID and latent quality (AUROC/TAD)\n- Produces interpretable prototype semantics\n- Enables diagnosis of unwanted correlations (e.g., hair color - smile);\n- Reveals temporal emergence of visual features.", "questions": "1. How stable are the prototypes across random seeds? Do the same semantic concepts consistently emerge?\n2. Can the authors quantify interpretability (e.g., localization or faithfulness metrics)?\n3. How does Patronus compare to direction-based interpretability methods (e.g., PCA directions, linear semantic editing)?\n4. Beyond hair - smile, does it detect other spurious links (e.g., makeup - gender cues)?\n5. How do performance and disentanglement vary with number of prototypes and prototype dim?\n6. Could the prototype activation vector be used for cross-domain transfer or editing (e.g., applying \"smile\" from one dataset to another)?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Combines a prototypical encoder and a conditional diffusion process, embedding interpretability directly rather than via post-hoc probing.\n- \"What, where, when\" decomposition is compelling and concretely supported by prototype visualizations and activation dynamics.\n- Prototypes correspond to semantically meaningful attributes (e.g., smile, hair color, collar) and can be manipulated for controllable image editing.\n- Demonstration of shortcut learning (hair color ↔ smile) is convincing and highlights societal relevance.\n- Ablations explore prototype distinctness, disentanglement, and conditional generation.\n- Figures and examples are intuitive and make the technical ideas clear.", "weaknesses": "- The prototype extraction is largely adapted from ProtoPNet, with limited theoretical or algorithmic innovation in how prototypes are integrated beyond being used as conditional guidance.\n- The interpretability claims rely mostly on qualitative visualization; no quantitative evaluation (e.g., localization accuracy, faithfulness, or human interpretability studies) is provided.\n- While visualizations are appealing, there is no test showing that manipulating a prototype truly corresponds to causal semantic change rather than correlated artifacts.\n- It’s unclear how many prototypes are needed, whether semantics are stable across seeds, or how sensitive the results are to prototype dimensionality.\n- Comparison is limited to DiffAE and InfoDiff. More modern interpretable or controllable diffusion baselines (e.g., DDPM inversion [1]) could be considered.\n- The paper could better position itself relative to prior encoder-conditioned approaches. What specifically differentiates the prototype activations from lower-dimensional semantic vectors used in DiffAE-like methods.\n- Theoretical claims in Section 3.5 (showing conditioning \"cannot degrade\" the ELBO) are informal and would benefit from a more rigorous derivation or empirical validation.\n\n[1] Huberman-Spiegelglas et al., CVPR’24.", "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": "60Jx9RQN3Y", "reviewer": "Reviewer_o2Si", "summary": "This paper introduces \"Patronus,\" a new type of diffusion model that tries to open up the \"black box\" of how these models generate images. The core idea is to build a \"prototypical network\" directly into the U-Net. This network learns a set of visual patterns (prototypes) from the data. As the model denoises an image, it maps its internal features to these prototypes, letting us see what patterns are being used, where they're being placed, and when (at which timestep) they appear. The authors show this can be used to understand the model's logic, catch it \"cheating\" by learning bad correlations (shortcut learning), and track how concepts form from noise.", "questions": "To help clarify these points, I have a few questions for the authors:\n\n1.\t**The Performance Trade-off**: Could you provide a direct, head-to-head comparison of generative quality (FID, etc.) and computational cost (GFLOPs, wall-clock time) between Patronus and a standard diffusion baseline of similar size?\n\n2.\t**The \"Shortcut Learning\" Claim**: This is a big selling point. Could you walk me through more concrete examples from your experiments (maybe from the medical dataset) where Patronus clearly identified an unwanted correlation that a baseline model was exploiting?\n\n3.\t**Scaling Up**: What are the real bottlenecks in scaling Patronus to something like ImageNet? Have you thought about or explored any approximations (e.g., to the prototype matching step) to make it more efficient at high resolutions?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Overall, the work is interesting. \n\n1.\tThe core idea is highly original. We’ve seen prototypes used for interpretability in classification, but applying this idea to understand the internal dynamics of a diffusion model as it generates is a very creative and insightful leap.\n\n2.\tThe ability to detect \"shortcut learning\" is a particularly high-impact claim, especially for the medical imaging applications they explore, where you absolutely cannot have a model focusing on the wrong artifacts.", "weaknesses": "I have a few key reservations that kept me from being completely convinced.\n\n1.\tMy main concern is about the classic \"interpretability vs. performance\" trade-off. The authors claim \"strong generative performance,\" but adding a whole prototype network inside the U-Net can't be computationally free. I really need to see a clear analysis of the cost. What’s the hit to the FID score compared to a standard baseline with the same parameter count? What's the extra latency during inference? This needs to be quantified.\n\n2.\tI'm also worried about scalability. The experiments mentioned seem to be on smaller-scale datasets (like CIFAR or low-res medical images). I'm skeptical that this method, as-is, would scale to high-resolution (e.g., 256x256 or 512x512) and high-complexity (e.g., ImageNet) datasets. The computational cost of computing patch-prototype similarity at every single step for large batches and high-res feature maps seems like it would be a major bottleneck.\n\n3.\tThe entire utility of this method hinges on the quality of the learned prototypes. The paper needs to do more to convince me that these prototypes are (a) genuinely meaningful to a human, (b) diverse and not just \"collapsed\" to a few simple textures (even though the authors directly use a loss function to make it diverse), and (c) actually represent what the model is really thinking. Qualitative examples can be cherry-picked, so I’m looking for a more quantitative analysis of prototype quality.\n\n4.\tThis method is only tested on unconditional diffusion models (here we do not treat the prototype activation vector as ‘real’ conditions). Could this method be applied to text/image conditioned diffusion models as well?", "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": "zzb74HaZ5Y", "reviewer": "Reviewer_hbds", "summary": "This paper introduces Patronus, an interpretable diffusion model that integrates a prototypical network to encode semantic concepts within visual patches. The model learns prototypes that represent localized semantic patterns and uses their activation vectors to guide the diffusion process. Unlike post-hoc interpretability methods, Patronus embeds interpretability directly into the architecture, allowing visualization of what prototypes represent, where and when they emerge during generation, and how they interact with semantic attributes. Experiments on multiple datasets show that Patronus achieves competitive or superior generation quality and meaningful prototype disentanglement.", "questions": "1. How stable is prototype learning when scaling the model size or dataset diversity? Are prototypes consistent across training runs?\n\n2. Can Patronus be extended to text-to-image diffusion models like Stable Diffusion, where semantics are conditioned on language rather than visual features?\n\n3. How sensitive are prototype visualizations to noise in the prototype activation vector? Do small perturbations yield consistent semantic control?\n\nActually I am not an expert on diffusion theory research but a researcher on diffusion modal application, so I will check the review from other reviewers and the responses from authors to adjust my final rating.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paper introduces an architectural approach to intrinsic interpretability in diffusion models through prototype learning, differing from previous post-hoc analyses.\n\n2. The integration between prototype activations and diffusion conditioning is mathematically consistent, with theoretical reasoning showing that adding the condition does not degrade likelihood.\n\n3. Evaluation on many datasets (including a medical imaging dataset) demonstrates both semantic interpretability and competitive FID performance.", "weaknesses": "1. Some theoretical explanations are verbose and could be streamlined; a visual overview of the training pipeline would improve accessibility.\n\n2. Joint optimization of prototype encoder, conditional DDPM, and latent diffusion may pose scalability issues for higher-resolution or text-conditioned tasks.\n\n3. The paper acknowledges that global attributes (e.g., age or gender) are not well captured due to the patch-based encoder, but offers no structural remedy. However, I think this is also acceptable for a short-term work.", "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)