Paper: Intriguing Properties of Generative Classifiers (ICLR-2024)

Reviewer comment (Novelty):
This is an analytical study of an existing approach with existing methods. The general direction of the findings is also not terribly surprising, although the exact degree of the results and the precision of this study is still useful I think.

Author response:
We are happy to hear that the reviewer appreciated the “precision of the study” we undertook in this paper. We believe (as also mentioned by reviewer e2eW) that “the significance of the empirical results are the key novelty of the paper”. In particular, we’d like to point out a few aspects where our empirical results are a significant and novel contribution, which can be assessed independently of prior expectations:
- This is the first study to compare modern generative and discriminative classifiers with large-scale human perceptual data on 17 challenging datasets
- The method sets a new SOTA in shape bias (first-ever human-like shape bias by a ML model)
- We demonstrate that generative classifiers achieve SOTA in human error alignment, even though deep learning models of human visual perception (and deep learning models in general) are currently dominated by discriminative classifiers
- We expect a number of different follow-up directions will emerge from this work: in the ML community, harnessing generative pre-training in combination with faster inference (perhaps even via distillation as you suggested?) for various downstream tasks; in the cognitive science community, studying perceptual illusions with generative models; in the neuroscience community, building more accurate models of how generative and discriminative processes might be combined in biological systems like the primate brain.

Reviewer comment (Experiments):
Have you thought about probing to find places where discriminative classifiers have an advantage over generative classifiers? Conceptually, I might try to think about types of images or classes which generative models are currently unable to generate well.

Author response:
We note that our experimental evaluation is unbiased since we are using a very broad existing benchmark of 17 datasets that is, in fact, a benchmark designed for discriminative models and not generative models. The results presented in the manuscript are, therefore, in no way cherry-picked in favour of generative models.

Within our experiments, we already see some places where discriminative models are better than generative counterparts: for example, as we observed in Section 3.2 and discuss in Section 5, generative models have a hard time classifying or generating rotated images while discriminative models are easily able to do this. Similarly, in the shape-vs-texture bias experiment, if the objective is to recognize the correct texture, discriminative models have a greater advantage over discriminative models. Finally, as mentioned in Jaini & Clark, 2023, on OCR like datasets (e.g. MNIST, and SVHN), discriminative models are able to achieve near-perfect accuracy while generative models still struggle (Stable Diffusion gets ~18% and Imagen gets ~79% on MNIST).

Reviewer comment (Experiments):
My primary concern is whether the alignment between the generative models and human behavior should be attributed to the models' generative nature or their rich semantic representations. In this context, a particularly relevant comparison is between CLIP and Stable Diffusion. Both models employ the same semantic representation, while CLIP is discriminative and Stable Diffusion is generative. However, CLIP demonstrates better OOD accuracy and error consistency than Stable Diffusion. Stable Diffusion outperforms CLIP as a human perception model only in the shape-bias benchmark. However, as suggested by the control experiment, this effect might be attributed to the high-frequency content of the additive noise. For Imagen, there is no corresponding discriminative counterpart; such a counterpart would require training a network to map images to T5-XXL encodings. Given the absence of such a control, Imagen's alignment with human perception does not provide strong evidence in favor of generative models over discriminative models. In summary, the reported results do not conclusively indicate whether human alignment necessitates generative modeling or merely rich semantic representation. The authors could strengthen the submission by addressing this issue, either through new experiments or by revising the conclusions and abstract to reflect the existing uncertainty.

Author response:
1. *Rich semantic representation:*

We agree that attributing findings to the models' generative nature or their rich semantic representations is interesting! We first note that CLIP is a contrastive based method that was trained to match images with captions; thus we don’t consider CLIP to be a discriminative model baseline; it was not trained for specific downstream tasks like object classification, but instead performs zero-shot classification. CLIP performs worse than Stable Diffusion in OOD accuracy and similarly in error consistency when CLIP uses a single text prompt rather than an ensemble of 80 different prompts. To make this clearer, we have updated Table 1 to include 1-prompt CLIP results which are directly comparable with the 1-prompt results for all generative classifiers in the paper.

By semantic representation, we assume you’re referring to the text representations? If so, this is unlikely to explain the high performance of the generative classifiers. While using a powerful text model may be important in settings involving complex language descriptions, the input prompts we use are very simple. It doesn’t seem likely to us that a 10b-parameter text encoder like T5-XXL offers much of an advantage when the prompt is “a bad photo of a dog.” In other words, the challenge of the out-of-distribution datasets we use is in the visual understanding needed to solve the tasks, not in language understanding. That said, we agree that a perfect comparison would include the same language encoder for every model, thus we have added the following to the paper’s limitations section: “Furthermore, different models use different language encoders which may be a confounding factor”.

Reviewer comment (Reproducibility):
The lack of an open code repository undermines the transparency and potential impact of the submission.

Author response:
3. *Open sourcing code:*

We agree with the reviewer and are working on open-sourcing code as soon as possible. We would additionally like to note that most components of our paper can be replicated already using existing open-source repositories by various different authors and repos:

The model-vs-human toolbox that contains all datasets, benchmarks, and various models: https://github.com/bethgelab/model-vs-human

Zero-shot classification code for Stable Diffusion: https://github.com/diffusion-classifier/diffusion-classifier

Finally, we have provided a high level of detail for reproducibility. A core experiment from our study (increased shape bias from diffusion-style training) has already been successfully replicated by an independent third party researcher based on our methods section. We provide the link to this independent replication with the caveat that they link to our publicly available non-anonymous version of the paper that might compromise the double-blind review process. Please do not visit the link if that is a concern: https://github.com/paulgavrikov/diffusion-noise-cls-pytorch

Reviewer comment (Experiments):
The loss guiding the classification score (Eq. 2) averages the error over the entire diffusion process. How does the model behavior compare if only the final, resulting image is considered?

Author response:
4. *Only considering final, resulting image:*

Maybe there is a misunderstanding: in our experiments we always evaluate the score based on the final denoised image (for example please see Figure 3 in Clark & Jaini 2023 on which our approach is based). We also do not perform sampling but just run the forward diffusion process to get scores. The averaging in Eq.2 is over the noise level -  we sample multiple noise levels to noise the target image and then denoise it to get the relevant scores. We have added an algorithm box in Appendix A to make this clearer.

Reviewer comment (Novelty):
The authors may wish to cite Golan, Raju & Kriegeskorte (2020, PNAS), which showed evidence of greater human alignment of generative classifiers over discriminative classifiers in simpler image domains.

Author response:
5. *Citing Golan, Raju & Kriegeskorte (2020):*

We have added a citation to the controversial stimuli paper in Section 3 (“These findings appear consistent with the MNIST results by Golan et al. (2020) reporting that a generative model captures human responses better than discriminative models.”)

Reviewer comment (Experiments):
Technically, it seems the paper applies different resizing resolutions for different generative models considered - e.g., 64x64 for Imagen and 512x512 for Stable Diffusion. I would like to see a discussion on the more reasons for this and its effect to the final performance.

Author response:
Imagen uses lower input resolution than Stable Diffusion because Imagen is a cascaded diffusion model and we only use the first stage image generator, not the superresolution models. The low resolution certainly is a disadvantage for Imagen in terms of performance, which we think makes its strong results all the more impressive. We have added the following note in the corresponding methods section on preprocessing (Section 2) to explain this choice: “ We preprocess the 17 datasets in the model-vs-human toolbox by resizing the images to $64 \times 64$ resolution for Imagen, $256 \times 256$ for Parti, and $512 \times 512$ for SD since these are the resolutions for the each of the base models respectively.”.

Reviewer comment (Experiments):
If possible, it would be helpful for the readers if there could be any discussion on practical considerations and design choices on the proposed method. It seems the current version ended up with computing pixel-wise mean-squared errors to measure conditional likelihoods, but wonder if there was other considerations in attempts to improve the performance of generative classifier. Such an ablation study would help for the future research on developing generative classifiers.

Author response:
We used exactly the same design choices as mentioned in Clark & Jaini (2023) for our experiments. We have now added a discussion on this in Appendix A.  

We use the L2 loss function for diffusion-based models since it approximates the diffusion variational lower bound (see Eq. 2) and thus results in a Bayesian classifier. Furthermore, both Stable Diffusion and Imagen are trained with the L2 loss objective. Thus, a different loss function will no longer result in a Bayesian classifier and will not work well due to differences from the training paradigm. We have added a discussion on the motivation for this choice in Appendix A.