Understanding the Limitations of Conditional Generative Models
Class-conditional generative models hold promise to overcome the shortcomings of their discriminative counterparts. They are a natural choice to solve discriminative tasks in a robust manner as they jointly optimize for predictive performance and accurate modeling of the input distribution. In this work, we investigate robust classification with likelihood-based generative models from a theoretical and practical perspective to investigate if they can deliver on their promises. Our analysis focuses on a spectrum of robustness properties: (1) Detection of worst-case outliers in the form of adversarial examples; (2) Detection of average-case outliers in the form of ambiguous inputs and (3) Detection of incorrectly labeled in-distribution inputs. Our theoretical result reveals that it is impossible to guarantee detectability of adversarially-perturbed inputs even for near-optimal generative classifiers. Experimentally, we find that while we are able to train robust models for MNIST, robustness completely breaks down on CIFAR10. We relate this failure to various undesirable model properties that can be traced to the maximum likelihood training objective. Despite being a common choice in the literature, our results indicate that likelihood-based conditional generative models may are surprisingly ineffective for robust classification.
paper summary: The authors claim that likelihood based generative models are not as robust to noise as general consensus claims them to be. To prove this authors make use of adversarial, ambiguous and incorrectly labeled in distribution inputs. Authors address issues regarding robustness in near perfect conditional generative models as well as assess the robustness of the likelihood objective. Pros of the paper: 1) Authors make well motivated arguments about how a near perfect generative model is also susceptible to attacks by providing examples that are adversarial, and have high likelihood and yet are incorrectly labeled. 2) They also demonstrate how class conditional generative models have poor discriminative power. Cons: 1) The experiments section is written very poorly. This section relies heavily on the supplement making it hard to read due to the constant back and forth between the results and details of the experiments. 2) Experiments seem largely limited. Comparisons on image data sets such as MNIST and CIFAR10 alone are not convincing enough to establish generalizability of the proposed theory. For example, the hypothesis could completely fail on text based generative models.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Understanding the Limitations of Conditional Generative Models
Class-conditional generative models hold promise to overcome the shortcomings of their discriminative counterparts. They are a natural choice to solve discriminative tasks in a robust manner as they jointly optimize for predictive performance and accurate modeling of the input distribution. In this work, we investigate robust classification with likelihood-based generative models from a theoretical and practical perspective to investigate if they can deliver on their promises. Our analysis focuses on a spectrum of robustness properties: (1) Detection of worst-case outliers in the form of adversarial examples; (2) Detection of average-case outliers in the form of ambiguous inputs and (3) Detection of incorrectly labeled in-distribution inputs. Our theoretical result reveals that it is impossible to guarantee detectability of adversarially-perturbed inputs even for near-optimal generative classifiers. Experimentally, we find that while we are able to train robust models for MNIST, robustness completely breaks down on CIFAR10. We relate this failure to various undesirable model properties that can be traced to the maximum likelihood training objective. Despite being a common choice in the literature, our results indicate that likelihood-based conditional generative models may are surprisingly ineffective for robust classification.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



paper summary: The authors claim that likelihood based generative models are not as robust to noise as general consensus claims them to be. To prove this authors make use of adversarial, ambiguous and incorrectly labeled in distribution inputs. Authors address issues regarding robustness in near perfect conditional generative models as well as assess the robustness of the likelihood objective. Pros of the paper: 1) Authors make well motivated arguments about how a near perfect generative model is also susceptible to attacks by providing examples that are adversarial, and have high likelihood and yet are incorrectly labeled. 2) They also demonstrate how class conditional generative models have poor discriminative power. Cons: 1) The experiments section is written very poorly. This section relies heavily on the supplement making it hard to read due to the constant back and forth between the results and details of the experiments. 2) Experiments seem largely limited. Comparisons on image data sets such as MNIST and CIFAR10 alone are not convincing enough to establish generalizability of the proposed theory. For example, the hypothesis could completely fail on text based generative models.
assistant
paper summary: The authors claim that likelihood based generative models are not as robust to noise as general consensus claims them to be. To prove this authors make use of adversarial, ambiguous and incorrectly labeled in distribution inputs. Authors address issues regarding robustness in near perfect conditional generative models as well as assess the robustness of the likelihood objective. Pros of the paper: 1) Authors make well motivated arguments about how a near perfect generative model is also susceptible to attacks by providing examples that are adversarial, and have high likelihood and yet are incorrectly labeled. 2) They also demonstrate how class conditional generative models have poor discriminative power. Cons: 1) The experiments section is written very poorly. This section relies heavily on the supplement making it hard to read due to the constant back and forth between the results and details of the experiments. 2) Experiments seem largely limited. Comparisons on image data sets such as MNIST and CIFAR10 alone are not convincing enough to establish generalizability of the proposed theory. For example, the hypothesis could completely fail on text based generative models.