Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality
Generative Adversarial Networks (GANs) are an elegant mechanism for data generation. However, a key challenge when using GANs is how to best measure their ability to generate realistic data. In this paper, we demonstrate that an intrinsic dimensional characterization of the data space learned by a GAN model leads to an effective evaluation metric for GAN quality. In particular, we propose a new evaluation measure, CrossLID, that assesses the local intrinsic dimensionality (LID) of input data with respect to neighborhoods within GAN-generated samples. In experiments on 3 benchmark image datasets, we compare our proposed measure to several state-of-the-art evaluation metrics. Our experiments show that CrossLID is strongly correlated with sample quality, is sensitive to mode collapse, is robust to small-scale noise and image transformations, and can be applied in a model-free manner. Furthermore, we show how CrossLID can be used within the GAN training process to improve generation quality.
 The paper is clear regarding motivation, related work, and mathematical foundations. The introduced cross-local intrinsic dimensionality- (CLID) seems to be naive but practical for GAN assessment. In general, the experimental results seem to be convincing and illustrative. Pros: - Clear mathematical foundations and fair experimental results. - CLID can be applied to favor GAN-based training, which is an up-to-date research topic. - Robustness against mode collapse (typical discrimination issue). Cons: -The CLID highly depends on the predefined neighborhood size, which is not studied properly during the paper. Authors suggest some experimentally fixed values, but a proper analysis (at least empirically), would be useful for the readers. - The robustness against input noise is studied only for small values, which is not completely realistic.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality
Generative Adversarial Networks (GANs) are an elegant mechanism for data generation. However, a key challenge when using GANs is how to best measure their ability to generate realistic data. In this paper, we demonstrate that an intrinsic dimensional characterization of the data space learned by a GAN model leads to an effective evaluation metric for GAN quality. In particular, we propose a new evaluation measure, CrossLID, that assesses the local intrinsic dimensionality (LID) of input data with respect to neighborhoods within GAN-generated samples. In experiments on 3 benchmark image datasets, we compare our proposed measure to several state-of-the-art evaluation metrics. Our experiments show that CrossLID is strongly correlated with sample quality, is sensitive to mode collapse, is robust to small-scale noise and image transformations, and can be applied in a model-free manner. Furthermore, we show how CrossLID can be used within the GAN training process to improve generation quality.
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>



 The paper is clear regarding motivation, related work, and mathematical foundations. The introduced cross-local intrinsic dimensionality- (CLID) seems to be naive but practical for GAN assessment. In general, the experimental results seem to be convincing and illustrative. Pros: - Clear mathematical foundations and fair experimental results. - CLID can be applied to favor GAN-based training, which is an up-to-date research topic. - Robustness against mode collapse (typical discrimination issue). Cons: -The CLID highly depends on the predefined neighborhood size, which is not studied properly during the paper. Authors suggest some experimentally fixed values, but a proper analysis (at least empirically), would be useful for the readers. - The robustness against input noise is studied only for small values, which is not completely realistic.
assistant
 The paper is clear regarding motivation, related work, and mathematical foundations. The introduced cross-local intrinsic dimensionality- (CLID) seems to be naive but practical for GAN assessment. In general, the experimental results seem to be convincing and illustrative. Pros: - Clear mathematical foundations and fair experimental results. - CLID can be applied to favor GAN-based training, which is an up-to-date research topic. - Robustness against mode collapse (typical discrimination issue). Cons: -The CLID highly depends on the predefined neighborhood size, which is not studied properly during the paper. Authors suggest some experimentally fixed values, but a proper analysis (at least empirically), would be useful for the readers. - The robustness against input noise is studied only for small values, which is not completely realistic.