Paper: Latent Diffusion Counterfactual Explanations (ICLR-2024)

Reviewer comment (Novelty):
It seems that the counterfactual explanation explained in the paper is very relavent to "semantic consistency" in [1]. I would suggest the author also discuss this paper.

Author response:
We are grateful that you brought this work [1] to our attention. We have included it in the updated manuscript. We would briefly mention the similarities as well as differences to our work for sake of completeness. Similar to our work, they also generate images from a latent space of a generative model that remain close to the original image. However, their focus is on obtaining positives for contrastive learning that maintain the semantics (i.e., class). In contrast, our work aims to understand which input features affect the classification of a classifier, thereby we also allow for (and promote) semantic changes.

Reviewer comment (Experiments):
How will the angular threshold affect the model performance? My concern is that if we mask too much then the model might loose too much information to reconstruct the image.

Author response:
The angular threshold controls how much the gradients of the classifier are allowed to deviate from the implicit classifier without being considered adversarial. In the case of large masking (i.e., small angular threshold) this would often remove such gradients and replace them with some overwrite value (in our experiments zeros). Thus, we would have little to no guidance from the classifier. Consequently, our method would only receive guidance from the distance gradients and thereby reconstruct the original image. On the other hand, in case of a large angular threshold, our guidance mechanism will bypass adversarial changes that will flip the decision of the classifier without a semantic change in the counterfactual image.

Reviewer comment (Writing):
The paper should be written in more formal way.

Author response:
We would appreciate it if the reviewer could point out parts of the submission that should be improved w.r.t. their formality.

Reviewer comment (Writing):
There lacks explanation on Algorithm 1 in the main body of the paper, which makes it hard to understand the technical connection between the proposed method and algorithm.

Author response:
As suggested by the reviewer, we added a brief explanation of Algorithm 1 in the main body.

Reviewer comment (Evaluation):
Some generated counterfactual examples seem to be unrealistic. (Possibly, the threshold-based gradient filter cannot properly filter out the adversarial gradients?)

Author response:
We agree that the generated counterfactual examples, e.g., Fig. 4(f), are not perfect. However, note that they are better than the ones from previous works, e.g., Fig. 3(h), where the problem of smoothing of high-frequency details is also prevalent. The deficiency in preserving high-frequency details in LDCE also stems from the application of the counterfactual generation within the lower-dimensional latent space. Here, the autoencoder’s decoder just fills in the high-frequency details. Thus, a better decoder, such as OpenAI’s recent consistency decoder [1], may do a better job filling in these fine details. Besides aforementioned, note that, as can be observed in Fig. 3(h), that changes in grass texture may also indicate a model bias, which may be desirable in the context of counterfactual generation.

Reviewer comment (Evaluation):
The authors argued that the proposed threshold-based consensus guidance mechanism filters out the meaningless adversarial gradients, but when it comes to Figure 4, it seems that the consensus guidance mechanism cannot filter out those gradients. For example, in Figure 4(f), intervening "bulldog" to "beagle" should not affect the grass texture since it should not be sensitive to the breed of dogs. It seems that those factors are not perfectly disentangled in the latent or noise space.

Author response:
We agree that the generated counterfactual examples, e.g., Fig. 4(f), are not perfect. However, note that they are better than the ones from previous works, e.g., Fig. 3(h), where the problem of smoothing of high-frequency details is also prevalent. The deficiency in preserving high-frequency details in LDCE also stems from the application of the counterfactual generation within the lower-dimensional latent space. Here, the autoencoder’s decoder just fills in the high-frequency details. Thus, a better decoder, such as OpenAI’s recent consistency decoder [1], may do a better job filling in these fine details. Besides aforementioned, note that, as can be observed in Fig. 3(h), that changes in grass texture may also indicate a model bias, which may be desirable in the context of counterfactual generation.

Reviewer comment (Evaluation):
Definitely, the proposed method is quantitatively outperformed in the CelebA case.

Author response:
The experiments on Celeb-A HQ demonstrate that our method, LDCE, is competitive to methods that are *specifically tailored* for this dataset, i.e., they use generative models pretrained on Celeb-A HQ. In fact, our method typically achieves the first or second rank on most metrics (Table 6) and is only inferior to ACE on Celeb-A HQ.

However, we find that LDCE is clearly superior to ACE on the more challenging ImageNet dataset (Table 2) at the same time. Arguably, “ImageNet is extremely complex and the classifier needs multiple factors for the decision-making process” [1, p.5] as argued by the authors of ACE. Note that this comparison is an apples-to-apples comparison of the methods, as LDCE-cls also uses a diffusion model that has been trained on ImageNet. It is worth noting that LDCE-txt, which uses stable diffusion as a diffusion model, is also superior to ACE.

Henceforth, we posit that the diminished performance observed on the domain-specific facial data from CelebA HQ can be attributed to the characteristics of the underlying diffusion model used in a specific counterfactual method. In fact, stable diffusion is renowned for its challenges in accurately generating faces and individuals [2]; a point openly acknowledged in both our limitations section and Appendix K. Nonetheless, the inherent merit of employing a non-domain-specific generative model - which has not been done before for counterfactual generation with generative models to the best of our knowledge - lies in its versatility, rendering it applicable across diverse datasets (as demonstrated on CelebA HQ). This flexibility becomes particularly valuable in real-world scenarios, where constraints on data accessibility are prevalent or pose significant challenges.

Though, if one has access to domain data, it is definitely beneficial to utilize it. To adapt our method, one could use low-rank adaptation that finetunes an existing stable diffusion model without the need of extensive computational resources. We expect that this will improve performance also on domain-specific data distributions, such as Celeb-A HQ.

Reviewer comment (Theory):
Could you provide more detailed explanation on Algorithm 1? How does it derived from Equation 10 and 11?

Author response:
As suggested by the reviewer, we added a brief explanation of Algorithm 1 in the main body.