Concept Bottleneck Generative Models
Authors: Aya Abdelsalam Ismail
Julius Adebayo
Hector Corrada Bravo
Stephen Ra
Kyunghyun Cho
Venue: ICLR 2024 poster (2024)
Keywords: Interpretability
generative models
Primary area: visualization or interpretation of learned representations
TL;DR: We extend Concept bottleneck models to generative models.

Abstract: We introduce a generative model with an intrinsically interpretable layer---a concept bottleneck layer---that constrains the model to encode human-understandable concepts. The concept bottleneck layer partitions the generative model into three parts: the pre-concept bottleneck portion, the CB layer, and the post-concept bottleneck portion. To train CB generative models, we complement the traditional task-based loss function for training generative models with a concept loss and an orthogonality loss. The CB layer and these loss terms are model agnostic, which we demonstrate by applying the CB layer to three different families of generative models: generative adversarial networks, variational autoencoders, and diffusion models. On multiple datasets across different types of generative models, steering a generative model, with the CB layer, outperforms all baselines---in some cases, it is \textit{10 times} more effective. In addition, we show how the CB layer can be used to interpret the output of the generative model and debug the model during or post training.

Published as a conference paper at ICLR 2024
CONCEPT BOTTLENECK GENERATIVE MODELS
Aya Abdelsalam Ismail .2 Julius Adebayo Héctor Corrada Bravo'
Stephen Ral.2
Kyunghyun Cho
Genentech Prescient Design
GGidd Labs
*Department of Computer Science, New York University
CCenter for Data Science, New York University
ABSTRACT
We introduce a generative model with an intrinsically interpretable layer-a con- cept bottleneck layer! -that constrains the model to encode concepts. The concept bottleneck layer partitions the generative model into three parts: the pre-concept bottleneck portion, the CB layer, and the post-concept bot- tleneck portion. To train CB generative models, complement the traditional task-based loss function for training generative models with a concept loss and an orthogonality loss. The CB layer and these loss terms are model agnostic, which we demonstrate by applying the CB layer to three different families of generative models: generative adversarial networks, variational autoencoders, and diffusion models. On multiple datasets across different types of generative models, steering a generative model, with the CB layer, outperforms all baselines in some cases, it is 10 times more effective. In addition, we show how the CB layer can be used to inter- pret the output the generative model and debug the model during or post training.
INTRODUCTION
Improvements in generative modeling have led to these models being applied to produce photo realistic images (Saharia et al., 2022), video (Ho al., 2022; Villegas al., 2022), protein se- quences (Ingraham et al., 2022), small molecules (De Cao and Kipf, 2018), and coherent text (Brown et al., 2020). However, current generative models admit litle-to-no-room for interpretation, which limits the ability to fix these models when they make mistakes. Consider model trained to generate protein sequences; a domain expert might be interested in determining whether the model has captured desirable features like thermostability and toxicity. An important goal is to use these features, as knobs, to steer the model to generate sequences that satisfy desired ranges of thermostability and toxicity. In this work, develop generative models with intrinsically interpretable components that can be used to simultancously interpret, debug, and steer the output of the model.
Challenges with interpreting and steering generative model representations. Current approaches for interpreting generative models cannot reliably indicate that a model's representations map to human-unde undaunnnddttttaablle features that the model relies on for its output. One approach (Belinkov, 2022) for interpreting a generative model's 's representations uses a low-complexity model to pre- dict a human understandable feature from the model's representations. However, high predictive performance of the low- low-complexity model does not indicate that the model's output relies on that feature (Lovering and Pavlick, 2022). Another approach constrains the model learn disentangled (Higgins et al., 2017; Tran et al., 2017; Meo et al., 2023) representations -tht is, representations that can be decomposed into independent factors. Nevertheless, it not possible to guarantee that a disentangled representation is human interpretable (Locatello al., 2019). Other approaches project model representations into lower dimensions and then search for directions correlated with human interpretable features (Härkönen et al., 2020); yet since the model was not constrained to learn such features, it is possible that its representations do not encode these desired features.
'The authors have contributed equally to this work and are listed in alphabetical order.
Code available

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(a) CB- CB-StyleGAN2 FFHQ
+ Feraaee
+ Glasses
(b) CB-DDPM on LAION Aesthetics
Rockwell
Van
Panther
Picasso
Figure 1: Steering using the CB layer for different architectures and datasets. The CB layer does hurt generation quality and can scale to high-dimensional settings. (a) concept bottleneck StyleGAN2 model trained on the FFHQ 1024x 1024 dataset. (b) A concept bottleneck diffusion model trained on the LAION Aesthetics 256x256 dataset, where we steer artistic style.
Concept bottleneck generative models (CBGMs). To address challenges with interpreting and steering current generative models, we present concept bottleneck generative models. In this genera- tive model, one of its internal layers-a concept bottleneck (CB) layer-is constrained to encode human-understandable features. We insert the CB layer into the generative model's architecture, the generative model can be divided into three parts: the pre-concept bottleneck portion, the CB layer, and the post-concept bottleneck portion. The pre-concept bottleneck portion maps from the input to activations, which the CB layer then maps into suman-understandee features. The pre-defined concepts alone can be incomplete, so we allow additional representational capacity for unknown concepts that constrained be orthogonal the pre-defined features. Lastly, the post-concept bottleneck layer maps both the output of the CB layer and unknown concepts to the generated output. To train CBGMs, complement the traditional loss functions for training generative models with two terms: concept loss and an orthogonality loss. The CB layer is model agnostic, which we apply different families generative models: generative adversarial networks, variational autoencoders, and diffusion models. Using the CB layer, we can demonstrate the following capabilities:
Steering Generative Models: By intervening on the output of the CB layer, we can modulate the level particular concept present the output generative model. We this capability control single concepts independently, and multiple concepts simultaneously. Figure shows an example of intervening on denoising diffusion probabilistic models and generative adversarial networks with CB layer. We can change the concept of the image while preserving the image quality. Across several datasets and types of generative models, steering a generative model with the CB layer outperforms several input conditioning baselines. We find that when the number of concepts increases the performance of conditioning models dramatically degrades while the performance of CBGMs remains almost unchanged; in such cases, can achieve up to 10x improvement steerability compared to traditional approaches.
Understanding and Debugging Generative Models: generative model during and post training. In Section 4.3, we show that the CB layer can help identify the important concepts that are responsible for model's generated output. Similarly, the output of the concept bottleneck (CB) layer can be used to distinguish model that has learned the pre-defined human-und unddarstndttttaaaalll features from model that has not.
The CB layer can also be used to debug
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2 SETUP & BACKGROUND
We now give an overview of concept bottleneck models and the types of generative models that consider. We assume that all training samples come with pre-defined human understandable features as such: {(z;, o=) where an example æ; E IR and an associated concept c; E R with k <<< d. Generative models consider the task of modeling the probability distribution of an observed random variable x P(x). We consider three generative model families: variational autoencoders (VAEs) (Kingma and Welling, 2013), generative adversarial networks (GANs) (Goodfellow, 2016), and diffusion models (Sohl-Dicksteinn et al., 2015; Song et al., 2020; Ho et al., 2020).
Concept Bottleneck Models (CBMs) & Concept Embedding Models (CEMs). CBMs (Koh et al., 2020) inserts an 'interpretable' layer into a deepnet. Specifically, CBMs map samples 2; to labels Y/ by first mapping z; to an intermediate representation c; = h(z;), where e₁ are understandable human concepts (e.g.. hair color). An 'interpretaalee label predictor, f. then maps the predicted concepts to labels: = f (h (z))- Consequently, the goal is to learn the following relationship
T2 3 G 4 yi- The functions and f can be learned jointly, sequentially, or independently. To improve the expressiveness of CBMs, (Espinosa Zarlenga et al., 2022) proposed CEMs, which map the input to high-dimensional representation for each concept.
Variational Autoencoder. VAEs (Kingma and Welling, 2013; Kingma al., 2019) consists of two components: an encoder and decoder. The encoder, (22, [x;)) maps an input, 2;, to a distribution over the latent variable, 2;- The encoder parameterizes a Gaussian density, from which we sample a latent vector z/ VAFs regularizes this latent distribution to be similar to the prior distribution
p(z), which is typically 2 ~ (O,,,) 1). The vector 2 is passed to the decoder, p(r;) (2f)), ). function that maps the latent vector distribution over the input. The task loss for the VAE is the negative log-likelihood:
vac = Dxz (1 (2/ (1p -E [(;;))())))
where DKL is the Kullback-Leibler divergence.
Generative Adversarial Networks. GANs (Goodfellow et al., 2014) consist of a generator G that captures the data distribution and a discriminator D that estimates the probability that sample came from either the training data or G. The generator maps noise vector 2; Conditional GANs (Mirza and Osindero, 2014; Chen et al., 2016; Odena et al., 2017) augment the input the generator with the concepts and also learn G(=,lc)). They, however, do not learn = h(;z). To learn Q h((z,,, we first encode x; into a latent vector ((z; (z;), since p(cs(a)) is known, i.e., concepts for given sample are known, we now can learn c = h(2;). Similar VAEs. we then sample new latent vector 2 and use this for generation G(2g) This approach has been widely employed (Larsen al., 2016; Isola et al., 2017; Wang et al., 2018) for image generation, For training, we use the loss introduced by VAE-GANs (Larsen et al., 2016), 6), which combines the VAE encoder regularization prior loss with a GAN loss.
output o; G(2,).
((xx) = DKr. (1 (z (.) (2:)) + IE [log D (a:)) + +E[1 - log (G(2)) (=]))
log
(2)
Diffusion models. Diffusion models can be interpreted latent variable models with two stages (Sohl-Dickstein et al., 2015; Ho et al., 2020). Given input data Iú, the first stage is the forward diffusion process, which involves incrementally adding Gaussian noise the input and can be described as: (t) = N (a'; vT ,,at,,11) where Br is determined according to pre-specified schedule. The second stage of the process learns denoising model, p (x-- (arf) " that reverses the forward process. The model is trained to maximize a lower bound to the marginal likelihood, which can be relaxed into mean-squared error loss as:
T
L(x)) (ur))task,i or Σ Blla(et,0) j (1), 9)
We refer to (Ho et al., 2020; Weng, 2021; Rogge and Rasul, 2022) for a more detailed overview of diffusion models.
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3 CONCEPT BOTTLENECK GENERATIVE MODELS
We propose insert concept embedding layer-which which term concept bottleneck (CB) layer into a generative model. Our overall framework, shown in Figure 2, consists of parts: the portion of the generative model before the CB layer (the pre-concept bottleneck network); the CB layer, and the portion of the generative model after the CB layer (the post-concept bottleneck network). The pre-concept bottleneck and post-concept bottleneck networks are specific to the type of model (GANs, diffusion, & VAE) used for generation, while the CB layer is common across all generative model families. We now discuss the architecture of the CB layer, its loss functions, and an intervention procedure for steering the generative model.
Pre-Concept Botteneck Natwork
Concept Bottleneck Layer
Contextc
Context,,
Contecto, Network
Contexfuxx Network
Network
Network
Contest,
Contort..
Probability,
Probability, cy Network
Probability, , Network
Network
Probability,
Probubliity,
Probubiityy
Probabilit..
Probubilty,.
Probabillty,
Context,
Pasl-Concspt Bottienack Network
Figure 2: Schematic of the concept bottleneck layer for generic generative model.
3.1 ARCHITECTURE
We adopt the CEM layer of Espinosa Zarlenga et al. (2022) to the generative model setting. However, unlike in supervised learning tasks where the concept set is assumed to be near complete (Koh et alll.22000 2020; Espinosa Zarlenga al., 2022: Yuksekgonul et al., 2022). it is unrealistic to expect the pre-defined human understandable ceatures-conneptss-- to be complete the generative setting. For example, consider generating a human face; finding comprehensive control every generation aspect is challenging. However, one might have available concepts such as hair color, eye color, and skin tone. We extend the CEM layer of Espinosa Zarlenga al. (2022) to encode for unknown concepts that might also be required for generation. The proposed modification of the concept bottleneck layer is shown in Figure 2, and consists of concept networks and an extra unknown-coneepp network.
of concepts that can
Concept Embeddings. Each concept is represented with two embeddings: w w, R representing the active and inactive concept states, respectively. The output of the pre-concept bottleneck portion, an embedding vector h, fed into context network, d. that maps h into two embeddings per concept. This context network can be viewed as two separate functions: wi = ++hh) (h) and w; x (hh). Embeddings w and w, are encouraged to be aligned with ground-truth concept c; by the function V trained predict the probability of concept c; being active from the joint embedding space, éN = V., (,(["" w, [") E [0, 1] (i.e, è is the predicted probability of concept c,). The final context embedding, W;. is weighted mixture of the two embedding w; = (Zaw, + (1 ()) w, All l concept embeddings are concatenated together, along with the non-concept embeddings, w = [wi, W2, Ws+1]. resulting in a bottleneck f(h) = w with size m(k + 1), which is fed into the post-concept network.
Adding the CB layer. We make the CB layer to be the first layer of the decoder for a VAE. Diffusion models typically follow U-Net structure, and the CB layer is inserted after the middle block of the U-Net. For GANs, we make the input to the generator to be output of the CB layer. We refer to Figure 6 in the Section A of the Appendix for additional discussion.
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Intervention Concept Probabilities. CBGMs support test-time interventions, which allows the user to steer the output of the generative model. To intervene on concept C, one replaces the probability of the concept being active, 6, with the desired probability &, The CB layers' context embedding, Wi = (ā,w, + (1 (4) w; ). is combined as a convex combination of the positive and negative context vectors. The new context embedding will then be passed the post-concept bottleneck model to generate the new output with the desired concept.
3.2
Loss FUNCTIONS & TRAINING
We train CBGMs in an end-to-end fashion by jointly minimizing the following loss function:
Liotal = Lrask + aeCeon + 3.otthh
where Lrask is the task loss, Leon is the concept loss, and Lotth is the concept orthogonality loss. The hyperparameters a, and B control the relative importance of the concept, and orthogonality losses. While the task loss is specific each generative model family, Lcon, and Lorth common across different models.
Task Loss: the task loss is the traditional objective function for each generative model family as defined in Equations 1, 2, and 3, respectively.
Concept Loss: across all generative model classes, the concept loss, Leons is the binary cross- entropy loss on the output of each probability network of the CB layer.
Concept Orthogonality Loss: An undesirable situation occurs if the unknown concepts are trans- formations of the known concepts, which hampers the ability to control the model's output. To prevent this, we encourage the unknown concepts to be orthogonal to the outputs of each concept network using orthogonality constraint (Ranasinghe et al., 2021) that minimizes the cosine sim- ilarity between the concept context embedding and the unknown context embedding as follows:
(w) Wx+1)
Corth
(5)
JEB
where (,, -) is the cosine similarity applied to two embedding, |. the absolute value, and B denotes mini-batch size and j denotes each sample the mini-batch The cosine similarity in the above equation involves the normalization of features such that (æi, xz) where || llz is 12 norm.
2.23
Tw.
4 EXPERIMENTS & RESULTS
In this section, we answer the following:
1. Stecrability: How well does the CB layer control the output of generative model, and how does stecring by intervening on the CB layer compare to traditional conditional generation methods?
2. Interpretability: How the CB layer be used to help interpret and debug generative model?
3. Generation quality & Ablations How does the inclusion of the CB layer affect generation quality? Also, how sensitive is the model's steerability each of the proposed components?
4.1 SETUP
Datasets. We consider the following datasets: (a) The 64 x 64 version of CelebFaces Attributes (Celeb-A), (Liu et al., 2015) that annotated with 40 attributes (e.g., male, smiling etc.); (b) curated subset of "aesthetic" LAION (Schuhmann et al., 2022) dataset that is annotated with concepts corresponding to one three categories: artistic style, presence a famous person, and fictional characters giving total of 155 concepts; (c) The Caltech-UCSD birds species (CUB) (Wah et al., 2011) that comes annotated with 312 concepts; (d) FlickrFaces -aaceessHHHrr (FFHQ) human faces dataset (Karras et al., 2019) consisting of 70.000 high-qualit, images 1024 resolution annotated with 8 concepts describing the face (c.g.. female, smiling, lipstick etc.); and (c) the ColOT-MNIST dataset (Deng, 2012), where take each label category and color as concepts.
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Generative Model Baselines. We consider three types of generative models: VAE, GANs, and diffusion models. We compare our method with each family's 's most commonly used conditional gen- eration approach. For GANs, consider CGAN (Mirza and Osindero, 2014), and ACGAN (Odena et al., 2017). For diffusion models, we compare with classifier (Dhariwal and Nichol, 2021) and classifier-free (CF) guidance (Ho and Salimans, 2022). For VAFs, we benchmark against conditional VAEs (CVAE) (Sohn et al., 2015),
4.2 STEERING CB GENERATIVE MODELS
We assess the ability of the CB layer to steer the output of the generative model effectively. As discussed in Section 3, to 'turn on' concept, we intervene on the concept probability, q.- We compare concept intervention via the CB layer to other prominent conditional generation strategies.
Experimental Setup We train concept classifiers to detect the presence of a concept in an input on the real data; we ensure that the minimum accuracy of any concept classifier at least 98% on a held-out test set from the real data. We sample noise latent vectors and then use them to generate images from the model. For each concept, we pass the generated image to the concept classifier, save the latent vector if the classifier predicts the concept to be absent (i.e., the probability of the concept present is less than 0.5), until we have 1000 samples. For each steering approach, we intervene to 'turn-on' the concept and generate new outputs. Finally, the original classifier again and measure the fraction of inputs for which it predicts the concept to be present; termed this metric the steerability accuracy. We examine the performance of models in two regimes: (a) Small balanced concepts regime: We extract subset of concepts with balanced labels across the dataset; for Celeb-A we extract the 8 most balanced concepts. and for CUB, we extract the 10 most balanced concepts. We then train models on the balanced subset. (b) Large unbalanced concepts regime: We train on all concepts regardless of their distribution in the dataset; here, we considered all 40 concepts for Celeb-A. We repeat each experiment 3 times and report the mean and variance in Table (per concept metrics is available Appendix) )
assess the newly generated images with
Concept Regime
Small balanced concepts
Large unbalanced concepts Celeb-A (40 concepts)
Dataset
CUB (10 concepts)
Celeb-A (8 concepts)
CGAN
5.4 # 0.4
8.7 + 1.3
2.9 + 0.0 1.2 + 0.1
ACGAN
18.5 : 0.4
9.2 + 0.7
CB-GAN
21.3
25.6
23.1 + 0.2
CF-Diffusion
2.7 +
7.15+33.8
5.14-2.4
CG-Diffusion
2.1 + 1.4
1.811.11
5,44:2.6
CB-Diffusion
14.8 : 6.2
13.8:12.7
12.6:1.7
CVAE
1.2 - 0.1
5.9411.6
5.3:t1.6
IB-VAR
10.7 4.6
16.3+1.11
15.5+3.3
Table 1: Average steerability metric for all concepts across three generative model families.
Results In the small balanced concepts regime, we find that steering via the CB layer is much more effective across different datasets and generative models when compared different standard conditioning approaches, in some cases up to 10x more effective, as shown on the CUB dataset for diffusion and VAEs. In the large unbalanced concepts regime, we find that the performance of conditioning models dramatically decreases with the increase in the number of concepts for example ACGAN accuracy drops from 9.2% to 1.2% when the model required to learn 40 concepts instead of 8 concepts; however using CB layer the performance degradation is minimal (at most 2.5%).
Scaling Input Size& & Number of Concepts We now test the effectiveness of CB generative models as we scale the input size, and the number of concepts. First, insert CB layer into the StyleGAN2 architecture and train the model on FFHQ 10242 dataset, Figure (a) shows samples generated from the model. We find that adding concept bottleneck layer does not affect the generation quality, and we are able to steer different facial concepts. Second, to test the effect of scaling the number of concepts, we train CB diffusion model curated subset of the LAION dataset, where each image is annotated with 155 concepts. In Figure (b), we demonstrate the effect of steering the artistic style. The first image in each example shows generated inputs without any concept intervention, while

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subsequent columns hold object attributes fixed and intervene in various artistic styles. These results suggest that concept bottleneck generative models can scale to contemporary settings.
4.3
INTERPRETABILITY
We now show how the concept- bottleneck layer can be used to (a) interpret the output of generative model and (b) debug the model during and after training.
4.3.1 INTERPRETING THE OUTPUT OF A CB GENERATIVE MODEL
Contemporary generative models are largely inscrutable and provide no way to identify the key concepts upon which they are reliant. In supervised learning, the CB layer partitions the output into concept basis to aid interpretability. We translate this insight to the generative model setting.
CB-StyleGAN2 FFHQ
(B) CB-VAE Celeb-A
(C) CB-DDPM on Color MNIST
Figure 3: Samples generated from different CB models along with their top three concept probabilities. The CB-layer can be used to interpret generated output. (a) CB-StyleGAN2 on FFHQ (b)CB-VAL model trained on Celeb-A. (c) CB-DDPM trained on the color MNIST.
Inspecting Sample Concept Probabilities. The CB layer partitions the pre-concept bottleneck representations into known and unknown concepts, which both determine the generated output. Similar to the supervised learning setting, the concept probability for the known concepts corresponds directly to the importance of a concept's embedding for a generated sample. This means that the most important concepts are those with the highest concept probability scores.
In Figure 3, we show samples from a LB-SttyeGAN2, EAE, and a CB-diffusion model trained on FFHQ, Celeb-A, and Color-MNIST, respectively. We plot the three concepts with the highest concept probability scores along with each sample. We observe a correspondence between the properties of the generated samples and the top-ranked concept probability scores. This suggests that the vector of concept probability scores helps identify the key factors the generative model relies on most.
4.3.2 MODEL DEBUGGING
Given a trained generative model, a challenging model debugging task seeks to determine whether the model captures concept of interest. For example, might be interested in determining whether the representation of model trained on Celeb-A captures hair color.
Debugging by tracking the concept loss. To determine whether CB generative model captures a desired concept, we track the concept loss (or accuracy) on a validation set during training. The trajectory indicates the level which the model can capture the concept of interest, Post-training, the concept probability score can also reveal the 'quality' of a model. A test-time debugging strategy inspects the per-concept probability histogram of randomly generated samples.
Experimental setup. We trained two models: Model-a on the ground truth set of concepts and Model-b on a modified dataset with randomized concept labels. The experiment was done on CB-GAN and CB-DDPM, trained on Color-MNIST and Celeb-A, respectively.
Training-time debugging. Figure 4, we show the training trajectory of the concept validation accuracy. The models trained on random concept labels fail to achieve above-random accuracy. Inspecting the performance curves allows us to differentiate these two models.
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Concest
1.00
(4) CI3-CAA tranned Dato--
(51 CS--ORMM trained Cadeb-A.
Figure 4: Concept loss trajectory when training a model on the ground truth versus corrupted concepts.
Test-time debugging. We train two CB-GAN models on Color-MNIST. Model-a is trained the ground truth concepts for both 'green' and 'red'. Model-b is trained on the corrupted 'red' concept and the ground truth 'green" concept. We plot the concept probability distribution as shown Figure 5; the defective model is unable to capture the red distribution, but the model trained on normal, non- randomized data does. Again, we find that such inspection helps differentiate these two classes of models.
Figure Concept Data Distribution.
4.4 GENERATION QUALITY ABLATIONS
The effect of adding CB layer generation quality: To test that a CB generative model does not affect generation quality, we compare the quality of the images generated by a CB generative model to an unconstrained model on Celeb-A (64x 64 version). We keep the input resolution, data pre-processing steps, and architecture fixed across all settings. From each method, we generate 40K ran- dom samples; we calculate Frechet inception distance (FID) (Heusel et al., 2017; Seitzer, 2020) between the synthetic images and the training dataset. Table shows FID scores across generative model classes. We observe that the concept bottleneck versions of the generative models have FID scores comparable to unconstrained generative models.
Gen Model
FID
StyleGAN2
9.0
StyleGAN2
Diffusion
CB Diffusion
9.3
VAE
8.4
CB-VAE
Table 2: FID scores for gener- ative models on Celeb-A.
Concept Loss Orthognality Loss
Unknown Embedding
Average Steerability
11.0
19.9
X
16.5
25.6
Table 3: Ablation showing the effect of removing different CBGMs components on the steerability metrics for CB-GAN. The last row indicates the proposed CB layer.
Ablations on CBGMs losses and components: We performed ablation experiments for the proposed loss functions to determine the relative importance of each loss term; we also added an ablation experiment to measure the effect of removing the unknown concept embedding. Ablations were done on CB-GANs. Results are shown in Table 3. Loss ablations we find that, as expected, the concept loss is most important, and without it, steerability degrades by about 15%. We observe a decline in steerability for the orthogonality loss as well, by 5.7 %. Unknown concept embedding ablation We find that removing the unknown concept embedding negatively impacts steerability, decreasing steerability 9.1 %; in addition, we observed large degradation in image quality when removing the unknown embedding FID score increased to 44.1 This indicates how important it is to have unknown concepts embedding since it is unrealistic to expect the pre-defined concepts to be complete (i.e, encode every aspect of generation) in the generative setting.
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5 DISCUSSION & CONCLUSION
Related Work. Concept-based interpretability approaches and concept bottleneck methods have been extensively investigated in supervised learning settings either post hoc (Kim al., 2018; Yuksekgonul et al., 2022) or as an interpretable-by-deee Losch al., 2019). Despite their benefits, CB layers are susceptible to feature leakage (Mahinpei et al., 2021; Margeloiu et al., 2021), which hampers steerability. Recent work has proposed alternative architectures to address leakage (Havasi et al., 2022; Zhang et al., 2022); Sheth and Ebrahimi Kahou (2024) proposed an orthogonality loss between known features to encourage the separation between the concept representations and to reduce the intra-concept distance. Here, we incorporate these ideas into and adapt the concept embedding layer of Espinosa Zarlenga et al. (2022) into generative models.
unit(Koh et al., 2020; Chen et al., 2020;
closely related work is the GlanceNets of Marconato et al. (2022), which incorporates a CBM into VAE architecture. GlanceNets enforce disentanglement among concepts and avoid feature leakage by ascertaining the out-of-domain inputs. Our formulation does not require disentanglement among concepts but only between the group of known and unknown concepts. Beyond a VAE, adapt the CEM to three families of generative models and scale them to more realistic settings. Overall, our proposal complements theirs, and the limitations of proposal leakage-can be addressed with their novel disentanglement and alignment formulation (Marconato et al., 2023).
Current (ext-to-image models (e.g DALLE, etc) (Radford al., 2021; Ramesh et al., 2022: Saharia et al., 2022) show remarkable conditional generation capabilities. However, our primary goal is not solely conditional generation; the CB layer enables capabilities that current text-to-image models do not enable: Model debugging, i.e., during training, we can identify which concepts the model can encode; Interpretability, i.e., why did my model generate this output? Instead of an outright replace- ment to current large-scale foundational models, this work is a stepping stone towards augmenting these models with intrinsically interpretable components that will make them easier to debug.
Steering via the CB layer can be seen as a form of model editing task that has renewed sig- nificance. Recent work has demonstrated intriguing control of classifiers (Santurkar et al., 2021), I), GANs (Bau et al., 2020; Wang et al., 2021; 2022), and large language models (Raunak and Menezes, 2022; Mitchell et al., 2021: : Meng et al., 2022). The main paradigm for editing first searches the model's latent space to localize human- iumaan-iteerpretable features, which are then manipulated for control. However, large-scale models can learn distributed representations, which makes effective localization challenging (Hase et al., 2023). Even if the localization strategy is effective, the latent representations of the generative model might not encode the interpretable feature. We circumvent these challenges by directly mapping interpretable features, which prevents the need for search.
I Limitations. Our proposed CB generative model does come with certain challenges: it requires that the training set be annotated with pre-defined concepts that can potentially be a laborious requirement in practice. Even though the CB layer can be applied broadly, we have only tested it for image tasks. Moving to text poses further challenges about what the nature of the concepts should be.
Conclusion. Due unprecedented improvements, there has been increased of generative models across various settings. However, these models are mostly inscrutable and difficult to steer. In this paper, we present concept bottleneck generative models, type of generative model where one of its internal layers-a concept bottleneck (CB) layer-is constrained to map from input representations human-understandaan
features. The CB layer can be used as simple plug-in module across different types of generative models. We show that inserting the CB layer does not hurt generation quality but helps to better steer and debug the models during and post- trainnraining. Overall, we see this work as a stepping stone for new kinds of generative models that are easier to understand and debug.

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Appendix
A
ADDITIONAL DETAILS ON CONCEPT BOTTLENECK LAYER
Diarenade
(e) VAE with CB layer
(o) GAN with CB layar
(c) Difusion with CB layer
Figure 6: Adapting concept bottleneck layer to different generative model families.
Adding the CB layer. Figure 6 shows where add the CB layer for each type of generative model. For the VAE Figure 6-a, the CB layer is the first layer of the decoder, meaning the output latent vector the encoder directly associated with pre-defined concepts. Similarly, in GANs Figure 6-b, the input to the generator contains the pre-defined concepts. Diffusion models typically follow U-Net structure, and the CB layer is inserted after the middle block of the U-Net shown Figure 6-c.
Continuous Variables. Our discussion in the main draft mainly addressed binary variables. Here, we discuss a simple extension of the current framework that also extends to continuous variables. First, we represent each continuous variable with single context vector instead of the positive and negative context vectors. Second, we transform each continuous variable so that normalized [0, 11. The output of the probability vector then the normalized continuous variable, Instead the binary cross-entropy loss function as before, we use the mean-squared error loss function. With these changes, we can now account for continuous variables.
Joint Categorical Variables. This can be extended to categorical concepts having context vector where j is the number of categories, i... each concept is represented as w!,... ...., w/:: for each class via Softmax function |é| ... 2/ = V V(w, [ w the final context vector constructed as the weighted mixture of all classes. In practice, we found that forcing sparse concept probabilities by adding temperature to the Softmax using Gumbel-Softun the probabilities more aligned with the ground truth concept distribution. For continuous variahles, the context network V would generate a single context vector, which would then be concatenated directly into final context embedding w; for interventions. an additional encoder is needed to encode the continuous variable to its context vector as done by Shoshan et al. (2021).
V, now predicts a probability
Jang et al. (2016) improves performance since this makes
B ADDITIONAL EXPERIMENTS
B. CBM IN DIFFERENT GENERATIVE MODELS
CB layer's model agnostic; however, the location of CB layer in particular architecture design choice. Below, we show how CBM can be inserted into special architectures like StyleGAN Karras et al. (2019; 2020) architectures and flow-based models.
B.1.1 CONCEPT BOTTLENECK STYLEGAN
Figure shows how a CB layer can be inserted into StyleGAN Karras et (2019); we found that the best location for the CB layer is after the mapping network so that all layers in the synthesis net- work take the context embedding as an input this allows high-quality image generation while ensuring that the model learns different concepts. Note that a similar location be used for StyleGAN2 Karras al. (2020) and StyleGAN3 Karras al. (2021); one would change the synthesis network accordingly.
trained CB- (B-StyleGAN2 Karras et al. (2020) on Celeb-A 64x64, and CUB 128x128. FID is computed between 40k generated images for each dataset is reported in Table 4, Samples of images generated from CBB-tyleGAN2 are given in Figure 8. We find that adding CB layer does degrade the quality of the generated images.
Experiments
Model
CUB
SUKKANN
CB SuleGANa
167
Table 4: StyleGANZ FID
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SINGAAND
Normaliee
Mappang netwoek
Syntheri nctwork
AEIN
FC
Uparzels
PC
W CD-Layer
Cosceptsi
Figure Adding CB layer to StyleGAN
Figure 8: Sample of imuges produced by StyleGAN
B.1.2 CONCEPT BOTTLENECK DDPM
Architecture: We consider the Denoising Diffusion Probabilistic Models (DDPMs) as diffusion model of choice. We follow the opensource implementation Rogge and Rasul (2022), which adapts the original DDPM implementation of Ho et al. (2020). The implementation follows a conditional U-NET architecture.
Dataset: We started with the entire collection of the LAION Aesthetics dataset. We then follow the Baio (2022) filtering process characters, celebrities, and artistic styles with at least 2500 samples, which resulted 155 concepts across the entire datasct. Next, we follow Brooks al. (2023)'s data preprocessing recipe but with the curated 155 concepts. Overall, we sample 100 samples for cach additional concept for cach of the 155 concepts arrive at 15500 samples in the second data processing step.
train CB-Diffusion model on the curated dataset following Baio (2022)'s strategy. In the second stage, we simply finetune the trained model on the collection samples obtained via Brooks ct al. (2023)'s 's training process. We found this two-stage strategy to be most effective for generating high-quality samples. Overall, training required 240 V100s gpus hours.
Training Strategy:
B.2 STEERING CB GENERATIVE MODELS
B.2.1
SINGLE-CONCEPT STEERABILITY
Figure and Figure 10 show images generated by CB-GAN and CB-StyleGAN2 before and after single concept intervention. We find that by changing the concept probability vector we can control the generated output.
Change Mole
Add
smila
Male
Smilng
Add wavy hair
Open Mouth
Mouth Sligtty open
Wavy hair
Figure 9: Single-concept interventions CB-GAN.
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Change Male
Remove Smile
Add Wary hair
Figure 10: Single-concept interventions CB-SttyeGAN2.
Small balanced concepts regime experiments: We report the per-concept steerability metric for the small balanced concepts regime experiments in Table 5. At a high level, for each class of generative model and across most features considered, find that controlling the presence of concept the generated output more effective with concept-botte bottlenckk generative models than current approaches. In GANs, prominent concepts like gender, steering the model output with the CB layer can five times as effective as compared to the closest baseline (ACGAN). In the diffusion model setting, the concept- concept-hottleneck bottleneck diffusion model outperforms classifier-fiet frreerrrrfeeeeeeeee di diffusion control across all 5 attributes considered. Similar results were also found in VAEs.
Class
High Cheekbones
Male
Mouth Open
Smiling
Wavy Hair
CGAN
5.8
0.9
6.0 0.6
6.1 0.9
3.6= 0.9
13.5
ACGAN
11.8+ 1.4
9.3+ 0.8
13.54 0.9
14.3+-111
8.4 0.9
InfoGAN
7.6 1 0.7
6.6 0.5
6.7
6.8 + 0.8
8.7 = 0.7
CB-GAN
9.8+ 0.9
53.7+
8.2+ 0.6
25.8+ 1.8
30.5+ 1.6
CF-Diffusion
8.3:14.1
10.2#5.5
7.2+4.66
7.1455.4
3.8+1.7
CB Diffusion
11.7=1.11
14.8122.4
13.9111.3
15.11:3.6
10.3::2.5
CVAE
4.8:122.4 12.5+1.4
3.5 14.3#3.1
3.9411.9
8.94-2.0
9.1:11.5
CB-VAE
14.2+2.6
19.4
15.7+2.4
Table 5: Steerability metric for each concept across three generative model families.
Large unbalanced concepts regime experiments: Figure 11 shows the per concept steerahility accuracy when models were trained on 40 Celeb-A concepts. Across all concepts CB-GAN outperforms other haselines.
CB-GAN
Steerability Accuracy
CGAN
60
ACGAN
InfoGAN
20
Concepts
Figure 11: Pre-concept steerability for large unbalanced concepts, 40 concepts on Celeb-A.
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B.2.2 MULTI-CONCEPT STEERABILITY
Figure 12 shows images generated by CB-GAN, we can add and remove multiple concepts from the generated images using the CB layer.
Open
Open Mouth
+ Male
Male
Mouth
Figure 12: Adding and removing multiple concepts in CBGMs
We report the steerability metric across five attributes in Table 6. For each setting of K, we intervene on the concept vector for one thousand inputs during generation and measure the induced change in attributes with iuributt--iddepeneent classifiers. We find that CB-GAN has average concept accuracy around 30%. across all experiments, which indicates that CB-GAN CB-GAN stecrability performance does not deteriorate with the addition of concepts, CB- CB-GAN all outperforms the baselines for up till K = 3. In the diffusion model and VAEs setting, adding a CB layer outperforms the baseline across all values of K.
Class
K=1
K=2
K=3
K=4
K=5
CGAN
7.1
11.30.4
18.1
+ 0.6
26.9 + 0.8
35.7
+ I.
ACGAN
+.2
13.1 0.5
25.0
0.7
40.6
55.0
+0.6
infogan
6,8 0.4
9.6
16.2
0.5
24.5
5 0.6
32.6+ 0.7
CB-GAN
29.3+ + 0.5
0.4
31.0 0.3
32.9
+ 0.4
36.0 0.4
CF-Diffusion
7.58-2.6
13.1=0.9
17.642.6
21.3
29.611,44
CB Diffusion
14.32-1.9
19.3-3.5
17.611.4
36.2-133
39.8:2.77
CVAE
6.241.37
14.5=3.1
1..3+1.4
24.7=11.9 3..3
34.8=33.6
CB VAE
15.14:2.46
18.9=2.3
27.614.8
41,610.9
Table 6: Multi-concept steerability metric.
B.2.3 CONCEPT ANNOTATIONS ABLATIONS
One of the main drawbacks of CBGMs is that it requires concept annotations for the entire training set. However, this requirement can be easily relaxed. CB layer can be trained with a small subset of data with concepts annotations. For samples with concepts annotations, we train the model with loss in Equation 4; for samples without annotations, we remove the concept loss Leon from Crotal so for such samples new loss is given as below.
Ceotal
Lrask + BLrrth
(6)
We repeated the experiment in section 4.2 for CB-GAN model trained on Celeb-A A 64 by 64, where we only use annotations for a subset of the training samples (note that here the models sees 100% of training samples but only a subset of samples is annotated), results are shown in Figure 13. We find that with about 20% of annotations we directly match steerability metrics of fully annotated training set. This result is not surprising: in the interpretability literature, concept bouteneck models classification papers Kim et al. (2018) and Yuksekgonul et al. (2022) have shown that it is possible to learn a high-performing concept classifier with as few as 200 samples.
Nouth Sightly
Smilng
Cheekbones
25-
60 CENO00OS
100
20
65 CO0CER55
80
300
20
85 of conooptss
60
100
20
4
60
82
325
21
Figure 13: Concept steerability metric for different percentage of concept annotations.
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B.3 INTERPRETABILITY
B.3.1
INTERPRETING THE GENERATED OUTPUT
Figure shows concept probabilities for different samples generated from CB- VAE and Diffuision on Celeb-A and color MNIST. By looking at each bar chart, one can understand which concepts were most effective in generating the output.
CB-VAE
CB-DDIM
Figure 14: Sample- wise interpretability offered by adding a CB layer for Diffusion model and VAE. The concept probability vector helps understand why the model generated particular output.
B.3.2 CORRESPONDENCE EXPERIMENT
We conduct a correspondence experiment to measure the alignment between the generated output and the concept probabilities. This experiment measures the ability of the concept probability communicate the feature importance of concept-bottennck generative model to end-user seeking to better understand model.
Overview & Experimental Details:
We generate 50 random samples and intervene on a subset concepts for these samples. For the Celeb-A dataset, we intervene on the male and smiling concepts. We chose these two concepts because they are the ones could reliably judge by simply inspection. Some of the other concepts, like attractiveness and high cheekbones, are more subjective. For the random and digit concept (out ten) at random to intervene on. then inspect these 50 samples and assess whether the intervened samples are retlected in the generated output. This assessment communicates the extent to which the probability histogram can be used as a reliable interpretation. Results: On the CB-VAE VAE trained on Celeb-A 64 by 64, we find an 87 percent correspondence, and 96 percent for CB-DDPM trained on COIST-MNIST. The consistent high correspondence scores suggest that the concept probability scores are an effective medium for communicating the generative model's 's sample dependent feature importance. By design. we constrain generative models to learn human understandable features precisely because we seek to enable feature importance communication between the model and a domain expert interested in debugging the model.
setting, select 1 color concept (out of two)
B.3.3 MODEL DEBUGGING
In the main paper, we showed that concepts during training. This can also be done by examining the concept training loss define main paper Section 3.2. Figure 15-a shows the training loss for different models the debugging experiment described in the main paper Section 4.4.2. Figure 15-a shows that both models were able to learn the green concept, i.c., training loss decreases for both models. Figure 15-b shows that only Model-a's 's training loss decreased for the red concept, while the training loss for Model-b remained high.
validation dataset to check if the CB CB-layer is actually leamning
Training concapt loss green concept
Training Concept Loss Concapt
Modea
Medid
0.6
0.0
0.2
022
2/5 66 1/.s
175
(n)
(b)
Figure 15: Model-a is trained the ground truth concepts. Model-b is trained the corrupted 'red' concept and the ground truth 'green' concept. (a) Low training loss on the green concept for both mod- els. (b) Model-a has low training for the red concept, but Model-b's 's training loss does not decrease.
18

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Published as a conference paper at ICLR 2024
C DEBUGGING VLMS VS CONCEPT BOTTLENECK MODELS.
In this section, designed synthetic debugging task, inspired by Tong al. (2023), demonstrate how concept bottleneck layers make easier debug generative models. This is comparison to standard text-to- image models that provide no way to determine the features that a model basing its output on.
Output of trained Intervention on:
Output of Fine sen--tuned Stable Diffusion XL For "Empty Cup" Prompt
"Empty", "Cup", and "Thomes Klokade" Concepts
Probability Histogram
Empty
Cup
T-kinkade
V-Gogh
00
02
06
08
10
Probability
Figure 16: Left: A sample image output from a fine-tuned Stable Diffusion XL for the prompt: "Empty Cup".
Overview and Experimental Design. Tong et al. (2023) found that most text-to text-to-image models find it difficult to generate images of an empty cup, and would typically generate glass half full instead. To replicate this finding, we prohe stable diffusion XI. for images of empty cup'. We manually inspect these images narrow down to set examples where the model makes a mistake and generates a cup with water instead. With the remaining corpus, we fine-tune the model on this corpus, which results in the model reliably generating images of cups that contain water when prompted for empty cup.
In the second phase of the experimental design, modify the LAION dataset inject two bugs: switch the labels for two artistic style concepts: Thomas Kinkade and Van Gogh. Sccondly, added all Ik images of the non-empty cup that we obtained from the stable diffusion model to the LAION training set. For this task, we only consider 4 known concepts: 1) Empty, 2) Cup. 3) Thomas Kinkade, and 4) Van Gogh. We then train the same CB-DDPM model discussed in the main text on this task. The model makes two mistakes:
When we intervene on Thomas Kinkade concept, generates images with Van Gogh artistic styles, and vice versa;
When intervene the concept 'empty', it generates images of cups that not empty since all the images tagged as empty in training set are not empty.
Result We show in Figure 16, the output from fine-ture tnn-ttund Stable Diffusion XI. for the prompt: "Empty Cup". Here we observe that the model indeed generates an output that is not empty. However, there is no way to understand why the model is making that mistake and to try to correct it.
Different from the standard text-Bt to to-image models that donot enable interpretation, histogram for input where the model generates empty cup, and have intervened the Thomas Kinkade concept. We observe that the model generates a non-empty cup, and Gogh style image. This immediately indicates that there likely an error with the Van Gogh and empty concepts in the dataset, and they are the sources of the errors. We can then inspect the training data to confirm these error..
inspect the probability
19


Reviews:
[{"review_id": "MSeoWCd5oS", "reviewer": "Reviewer_vC1Y", "summary": "The paper proposes a concept bottleneck (CB) based approach to make generative models interpretable. Specifically, it proposes to insert a CB layer into generative models to constraint (most of) their internal representation to a certain concept set, making it quantifiable with respect to the concepts. The key challenge the paper highlight is that features to perform generative modeling may not be always interpretable. To address this, the paper propose to extend previous Concept Embedding Models (CEMs) to additionally encode an \"unknown\" concept that is meant to be orthogonal to any other concepts. The experimental results show that the proposed approach achieves better accuracy on steering concepts compared to other conditional generative modeling approaches, and providing additional features such as interpretability and debugging of the individual models.", "questions": "- The paper mentions Platt and Barr (1987) regarding hyper-parameter optimization. Does it mean that the paper actually applied that method for tuning in the experiments? If so, it may be good to provide an overview on the method in the paper as well. \n- It seems making sure the minimal concept orthogonality loss is crucial for the soundness of the method, otherwise there can be an overlap between the given concepts and the \"unknown\" concept. One immediate ablation one can try is to check whether the loss is minimized is to strictly project the learned unknown context vector to be orthogonal to other vectors and see if there is degradation in performance. Or I think this kind of procedure can be even incorporated into the training phase to guarantee the minimal concept orthogonality loss. Any discussion regarding the actual orthogonality of the learned unknown context vector would be helpful for the readers.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper addresses an important yet under-explored problem of interpreting generative models.\n- I found the idea of introducing unknown concept vector is interesting and novel. \n- The proposed method is widely applicable for diverse model families, including VAEs, GANs, and Diffusion models. \n- The paper also partially demonstrates the scalability of the method with a LAION subset on Diffusion model, following contemporary practices.\n- The effectiveness of the method is clearly validated through experiments.", "weaknesses": "- The paper claims at Abstract and Introduction that the proposed method is model agnostic, but I am not certain on that. For example, it seems to me that applying the CB layer to other types of generative models can be non-trivial, e.g., to normalizing flows and invertible models. \n- Although the paper presents some scalability experiments, the other parts of the experiments can be seen as somewhat limited in their scale. For example, the paper only explores simple GAN architectures not covering modern ones such as StyleGAN-2. I am also wondering why more recent methods such as GAN inversion could not be a baseline. If these point can be addressed, it will be also beneficial to support the model-agnostic aspect of the method. \n- More qualitative comparisons could be added and highlighted for the steerability experiments, given that the quantitative results are dependent on the pre-trained concept classifiers. \n- (minor) The definition of concept orthogonality loss, Eq (5), is written in somewhat confusing manner - e.g., the index j is not used in the definition.", "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": "bg9eUFmhtN", "reviewer": "Reviewer_9WtF", "summary": "This paper studied concept bottleneck on a number of generative models, like VAE, GAN, and Diffusion. The work shows using concept bottleneck enables steering generative models and debugging better.", "questions": "1. How does the method tackle unseen concepts? Can this be extended to open-world concepts?\n\n2. How long does diffusion training take? Do you train all generative models from scratch?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The idea of introducing a concept bottleneck layer to generative models is very interesting. While this has been studied in discriminative models, this is the first time that the reviewer see concept bottleneck been used in generation, which provides explanation and control over the model generation.\n2. It shows better steerability than existing methods, like InfoGAN. Based on Table 1, CB-GAN has higher accuracy of the concept classifier. InfoGAN was a distangled GAN that allow control over concept in an unsupervised way.\n3. Experiments are conducted on three types of generative models, which shows the method is general.", "weaknesses": "Lacking baseline comparison with SOTA model. Without concept bottleneck, exiting work can also control concept, like text-image diffusion, GANSpace, StyleGAN, and others.\n\nLike one can find a direction in StyleGAN that control specified concept, which is also very effective.\n\nSimilarly, show comparison to stable diffusion, GANSpace.", "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": "aBE85UZDsZ", "reviewer": "Reviewer_YbNz", "summary": "This paper proposes to introduce a \"concept bottleneck\" layer to generative models, allowing for better control and introspection of the generative process. The is demonstrated to work with several generative models - VAEs, GANs and diffusion models. In all method the encoded latent is fed to a set of \"concept networks\" - one for each conceptual property like colour, gender etc. - which are trained to project the latent on an embedding and predict the presence or absence (learned seperatly) of a specific context (with ground truth data provided as targets, i.e supervised). The generative model is trained in conjuction with the concept layers by using a linear combination of the concept embeddings weighted by their presence (or absence) probabilitliies. as input to the decoder (instead of the original latent). There is an additional orthogonality loss which constrains the concept embeddings to be orthogonal to an \"unknown\" concept embedding.\n\nControllability is achieved by constraining the presence or absence probabilities to a specific value and feeding the resulting combination to the generative process. Interpretability is achieved via inspecting the resulting presence / absence probablilites for different concepts.\n\nThe method is evaluated on several datasets with ground truth concept data and is shown to improve on baselines.", "questions": "* How does the method scale with the number of concepts? does it affect performance of the generative model?\n* Are all concepts equal? are there ones that affect the output of the model more than others?\n* What is the role of the \"unknown\" concept in generation? what happens if you steer it towards \"presence\" (if possible?)", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "I think the paper has an interesting premise and good motivation - interpretability and controllability of generative models are important subjects and structured approaches to this can be useful.\n\nI enjoyed reading the paper and it is largely well presented and written.", "weaknesses": "Unfortunately the paper suffers from several weaknesses:\n\n* The concept bottleneck layer requires ground truth data - this is fine for small-ish scale data, but we can't hope to obtain this kind of data for large datasets. This puts some doubt to the usefullness of the model.\n* The model requires a *separate network* for each concept - this is fine as long as there is a small number of concepts but I doubt this is a scalable approach going forward.\n* Because the model is supervised it will only learn about concepts provided through the labels - there could be an argument that concepts should be learned from the data unsupervised as we don't necessarily always know what are the underlying factors.\n* Evaluation is a bit weak in the paper - Table 1 which is arguably the main quantitative result of the paper is not well explained. If I understand correctly these are the prediced concept presence probablilites after the model has been constrained to include them. This is problematic because a) we don't know the accuracy of presence classifier and b) measuring only the probablitiy doesn't tell us if the output actually contains the desired concept (i.e - there are no visualizations). \n* In absolute terms, the results in Table 1. are quite poor - I would expect much higher accuracies.", "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": "UR4QVyJxkg", "reviewer": "Reviewer_5NVe", "summary": "The authors propose to introduce a concept-bottleneck layer into deep generative models, including GANs, VAEs, and diffusion models.  The bottleneck is encouraged to encode interpretable concepts using dense annotations.  Furthermore, it is paired with an unsupervised side-channel conveying information that is constrained to be orthogonal to the concepts in the bottleneck.  The resulting generative model, CBGMs, can be steered by controlling the concepts themselves and admits investigating its behavior by intervening on the concepts.  The experiments evaluate steerability, interpretability, debuggability, and generation performance against a selection of conditional generative models.\n\n**Post-rebuttal update**: I have increased my score based on the rebuttal.", "questions": "Please see my doubts about the experimental setup in the **Quality** paragraph above.  I'd appreciate if you could clarify what motivated the limited choice of data sets, for instance.  Also, please let me know if I got it wrong and missed results that are in fact reported in the paper.\n\n**Score**: I graded the paper 3/10, but I think of it as a 4/10 (there is no such option in openreview).  I *am* willing to increase the score if solid motivations are provided for the interpretability, debuggability, and FID score of CB-* models on data sets besides those reported here, or any other solid indication that CB-* models hold their promises beyond the few data sets considered here.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "**Originality**: The specific architecture (or family of architectures) proposed here is novel.\n\n**Quality**: The idea is sensible and the writing is good.\n\n**Clarity**: The narrative (excluding the experimental section, which lacks some details, see weaknesses) is very clear.  \n\n**Significance**: At a high level, this paper fills a clear gap in the CBM literature, showing how the concept bottleneck idea can be extended beyond discriminative models.", "weaknesses": "**Originality**: The idea of using concepts for steering generative models is by now somewhat familiar tho, as this is what VLMs implicitly do, and what conditional generative models have done for a while (the whole motivation for investigating disentanglement stems precisely from this problem, although I agree disentanglement does not guarantee interpretability).  The idea of integrating VAEs in concept-bottleneck classifiers has also been explored (as mentioned in the related work), but not for the purpose of steering generative models.\n\n**Quality**: The critical issue with this paper is that the experiments are lacking.\n\nFor instance, the experiment in Section 4.2 measures steerability only by turning concepts on (but not off).  It is not clear how to understand steerability:  it is defined as the accuracy of a pre-trained concept detector on generated images after turning on a concept that originally was predicted to be off.  Why the accuracy?  Why not the percentage of images in which the concept is predicted to be on?  What would be the ideal value, 100%?  If so, the numbers reported in the able are (better than the competitors but) quite far from this goal.  Also, the validation accuracy of the concept classifiers used to evaluate steerability is not reported, so how do we know they are high quality?\n\nUnless I missed something, interpretability is only assessed qualitatively (Section 4.3.1, Figure 4).\n\nGeneration quality (Section 4.4) is only measured for a single data set.  Same for debuggability in Section 4.3.\n\nOverall, the choice of research questions is okay, but taken individually the experiments leave something to be desired.  Evidence is provided for individual data sets or even selected examples.  This makes it difficult to assess the true limitations of CB-* models.\n\n**Significance**:  What is not entirely clear to me is what niche of problems CBGMs help with that VLMs cannot to some extent already deal with.  The other issue is that concept annotations -- as readily admitted by the authors -- is difficult to acquire.  This is why, recently, researchers have started defining concepts using VLMs like CLIP.  Mind you, I am not an LLM enthusiast -- but I feel there is something anachronistic about the proposed setup.  Still, I am a fan of concept bottleneck models and I like the idea of broadening their applicability.  The only serious issue is with the experimental evaluation.", "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)