Evaluating generative models is tricky. Without ground truth, we rely on metrics like Inception Score and Fréchet Inception Distance to assess image quality and diversity. These metrics help balance the trade-off between coherence and uniqueness in generated samples.

Human evaluation and visual inspection techniques complement quantitative measures. Choosing the right metrics depends on the model type, task requirements, and available resources. Combining multiple approaches provides a more comprehensive assessment of generative model performance.

Challenges and Metrics in Generative Model Evaluation

Challenges in sample evaluation

• Ground truth absence complicates direct comparison of generated samples
• Quality assessment subjectivity influenced by personal and cultural biases
• Quality-diversity trade-off balances coherence with output uniqueness
• Mode collapse limits sample variety, requires careful detection
• Computational demands for evaluating large datasets (ImageNet, COCO)

Quantitative metrics for image quality

• Inception Score measures quality and diversity using pre-trained Inception v3 model $IS = exp(E_{x~p_g}[KL(p(y|x) || p(y))])$
• Fréchet Inception Distance compares real and generated image statistics $FID = ||\mu_r - \mu_g||^2 + Tr(\Sigma_r + \Sigma_g - 2(\Sigma_r\Sigma_g)^{1/2})$
• Kernel Inception Distance employs kernel methods, robust for small samples
• Precision and Recall for Distributions evaluates quality and diversity separately
• Improved Precision and Recall uses k-nearest neighbor approach for enhanced assessment

Qualitative methods for model assessment

• Human evaluation leverages crowd-sourcing platforms (Amazon Mechanical Turk) and expert opinions
• Visual inspection techniques include side-by-side comparisons and latent space interpolation
• Attribute manipulation assesses controlled changes and latent representation disentanglement
• Task-specific evaluations measure performance in downstream tasks (image classification, object detection)

Selection of task-specific metrics

• Model type considerations for GANs, VAEs, flow-based models
• Task requirements guide metric choice (IS for images, BLEU for text, PESQ for audio)
• Domain expertise integration develops custom metrics for specialized applications (medical imaging, financial forecasting)
• Resource constraints balance metric accuracy with computational cost
• Complementary metrics combine quantitative and qualitative evaluations
• Benchmark datasets enable fair comparisons (MNIST, CIFAR-10, ImageNet)