- The paper proposes an improved precision and recall metric that separately quantifies sample quality and diversity using explicit non-parametric manifold representations.
- It employs a k-nearest neighbors approach with VGG-16 feature embeddings to accurately assess generative model performance and mitigate issues like mode collapse.
- Empirical results on StyleGAN and BigGAN demonstrate the metric’s robustness, clearly revealing tradeoffs between precision and recall across diverse image generation scenarios.
Improved Precision and Recall Metric for Assessing Generative Models
This paper proposes an enhanced metric for evaluating the performance of generative models, particularly focusing on precision and recall in image generation tasks. The authors aim to address limitations in existing metrics like FID, IS, and KID, which aggregate the quality and diversity of generated samples into a single value, often obscuring the tradeoffs between these two aspects.
Main Contributions
The primary contribution is an improved precision and recall metric for measuring sample quality and variance separately. Unlike prior work by Sajjadi et al., this approach does not rely on relative densities, which often fail in scenarios like mode collapse or truncation. Instead, the metric uses explicit non-parametric manifold representations of both real and generated data distributions.
Methodology
The authors employ a k-nearest neighbors (k-NN) approach to determine whether a generated sample fits within the real data manifold and vice versa. By embedding images into a feature space using a pre-trained VGG-16 classifier, they form adaptive-resolution manifold approximations. Precision is calculated by querying if a generated image is within the real image manifold, while recall queries if a real image falls within the generated image manifold. This method circumvents issues tied to prior density-based metrics.
Empirical Validation
Experiments were conducted using StyleGAN and BigGAN models to illustrate the efficacy of the proposed metric. For StyleGAN, four setups with varying levels of truncation were analyzed, showing the metric's capacity to distinctly measure differences in sample quality and diversity. The results corroborate expected behaviors: high truncation yields high precision but low recall, whereas models optimized for FID may show high recall but reduced precision.
In BigGAN, precision and recall were examined for different ImageNet classes, validating the metric's robustness across diverse and challenging datasets. High variation classes showed higher recall, while precision remained consistent for simpler classes, aligning with visual inspection results.
Implications and Future Work
This metric provides a nuanced understanding of generative models by separately quantifying sample quality and diversity. It has implications for improving model architectures and training configurations, as demonstrated in the analysis of StyleGAN design variants. The inclusion of individual sample quality estimation allows for further applications in interpolations and realistic image assessments.
Future research may explore the utility of this metric in broader applications, such as image-to-image translation, and in-depth studies on the impacts of different training configurations and truncation strategies.
Conclusion
By presenting an approach that maintains distinct measures of quality and diversity, this work enriches the toolkit for evaluating generative models, fostering a deeper understanding of their performance characteristics. The metric is anticipated to assist in designing models that better balance realism and coverage, thus enhancing the generalizability and applicability of generative techniques.