Are GANs Created Equal? A Large-Scale Study (1711.10337v4)

Abstract: Generative adversarial networks (GAN) are a powerful subclass of generative models. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is still very hard to assess which algorithm(s) perform better than others. We conduct a neutral, multi-faceted large-scale empirical study on state-of-the art models and evaluation measures. We find that most models can reach similar scores with enough hyperparameter optimization and random restarts. This suggests that improvements can arise from a higher computational budget and tuning more than fundamental algorithmic changes. To overcome some limitations of the current metrics, we also propose several data sets on which precision and recall can be computed. Our experimental results suggest that future GAN research should be based on more systematic and objective evaluation procedures. Finally, we did not find evidence that any of the tested algorithms consistently outperforms the non-saturating GAN introduced in \cite{goodfellow2014generative}.

• The paper provides a comprehensive empirical evaluation of multiple GAN architectures using over 6.85 GPU-years and standardized metrics.
• It reveals that ample computational budget and rigorous hyperparameter tuning reduce performance differences across GAN models.
• The study highlights limitations of traditional metrics like IS and FID, advocating for combined precision-recall evaluations for robust analysis.

Are GANs Created Equal? A Large-Scale Study

Mario Lucic, Karol Kurach, Marcin Michalski, Olivier Bousquet, and Sylvain Gelly present a comprehensive empirical paper that investigates the effectiveness of various GAN architectures using standardized evaluation metrics and extensive computational resources. This paper provides a nuanced perspective on GAN performance, underscoring the importance of systematic and neutral comparisons.

Major Contributions

1. Comprehensive Empirical Analysis: The paper employs a large-scale, systematic evaluation of state-of-the-art GANs. By conducting over 6.85 GPU-years worth of experiments, the authors deliver robust insights into the performance spectra of different GAN models.
2. Emphasis on Computational Budget: A significant revelation is that model performance disparities dwindle when given ample computational resources and parameter optimization, suggesting that many advancements in GAN research might be computational rather than algorithmic.
3. Proposed Evaluation Metrics: To tackle the limitations of current evaluation metrics, the authors propose using datasets where precision and recall can be computed effectively, alongside the existing FID metric.

Evaluation Metrics

The paper assesses GANs using two main metrics:

• Inception Score (IS): This metric evaluates the quality of generated samples based on a pre-trained classifier. Despite its widespread use, it suffers from insensitivity to intra-class mode collapse.
• Fréchet Inception Distance (FID): FID measures the similarity between the distributions of real and generated data, offering better sensitivity to mode collapse and sample diversity. This robustness underscores its suitability over IS for a more reliable evaluation.

Experimental Setup

Hyperparameter Sensitivity and Budget Constraints

• Architecture and Hyperparameters: The paper uses a consistent architecture across all models, ensuring a fair comparison. Both wide (100 samples) and narrow (50 samples) ranges of hyperparameters are explored.
• Impact of Initialization: The robustness of GANs to random initialization is highlighted, indicating significant variance in performance due to different random seeds.

Data Sets and Task Complexity

• The paper uses classic datasets such as MNIST, CIFAR-10, Fashion-MNIST, and CelebA to cover a range of complexities in generative modeling tasks.
• A novel set of tasks involving convex polygons was introduced to explore how effectively GANs capture low-dimensional data manifolds. This setup allows for the computation of precision and recall, providing an additional layer of evaluation.

Key Findings

1. No Dominant Model: The paper finds that no single GAN architecture significantly outperforms others across all datasets. Performance variations are often a product of hyperparameter optimization and the computational budget allocated.
2. Importance of Budget: Results indicate that with enough computational budget, different GAN models tend to converge to similar performance levels. This observation implies that the reported superiority of certain models might be contingent upon the budget rather than intrinsic algorithmic improvements.
3. Limitations of FID and IS: Although FID is robust to noise and better at detecting mode collapse than IS, both metrics fail to discern overfitting scenarios where models might memorize training data. The paper suggests combining FID with precision-recall approximations for a more comprehensive evaluation.

Future Directions

1. Larger-Scale Studies: Research should extend to higher-resolution datasets and more complex neural architectures to identify potential distinctions among GAN models.
2. Metric Refinement: Developing more nuanced and robust evaluation metrics that can better detect overfitting and other failure modes in generative models is necessary. Metrics that are invariant to the specifics of the encoding network used could provide more generalized insights.
3. Exploration of Optimization Techniques: Beyond random search, studies incorporating more sophisticated optimization methods like Bayesian optimization could yield further insights into the hyperparameter sensitivity of GANs.

Conclusion

This paper provides a detailed, empirical evaluation of GANs, demonstrating the critical role of computational budget and hyperparameter tuning in determining performance. It sets a precedent for future GAN research to focus on more systematically rigorous and unbiased comparisons. The authors advocate for the adoption of combined metrics and encourage increased computational resources to facilitate more reliable model evaluations.