Updated on 2024-10-28

Author: lxaw

papers/list.json

@@ -2,7 +2,7 @@
   {
     "title": "Improved Precision and Recall Metric for Assessing Generative Models",
     "author": "Tuomas Kynkaanniemi et al",
-    "year": "1192",
+    "year": "2019",
     "topic": "generative models, precision, recall",
     "venue": "NeurIPS 2019",
     "description": "This paper introduces an improved metric for evaluating generative models by separately measuring precision (quality of generated samples) and recall (coverage/diversity of generated distribution) using k-nearest neighbors to construct non-parametric manifold approximations of real and generated data distributions. The authors demonstrate their metric's effectiveness using StyleGAN and BigGAN, showing how it provides more nuanced insights than existing metrics like FID, particularly in revealing tradeoffs between image quality and variation that other metrics obscure. They use their metric to analyze and improve StyleGAN's architecture and training configurations, identifying new variants that achieve state-of-the-art results, and perform the first principled analysis of truncation methods. Finally, they extend their metric to evaluate individual sample quality, enabling quality assessment of interpolations and providing insights into the shape of the latent space that produces realistic images.",

papers_read.html

@@ -108,7 +108,7 @@ <h1>Here's where I keep a list of papers I have read.</h1>
             <tr>
                 <td>Improved Precision and Recall Metric for Assessing Generative Models</td>
                 <td>Tuomas Kynkaanniemi et al</td>
-                <td>1192</td>
+                <td>2019</td>
                 <td>generative models, precision, recall</td>
                 <td>NeurIPS 2019</td>
                 <td>This paper introduces an improved metric for evaluating generative models by separately measuring precision (quality of generated samples) and recall (coverage/diversity of generated distribution) using k-nearest neighbors to construct non-parametric manifold approximations of real and generated data distributions. The authors demonstrate their metric&#x27;s effectiveness using StyleGAN and BigGAN, showing how it provides more nuanced insights than existing metrics like FID, particularly in revealing tradeoffs between image quality and variation that other metrics obscure. They use their metric to analyze and improve StyleGAN&#x27;s architecture and training configurations, identifying new variants that achieve state-of-the-art results, and perform the first principled analysis of truncation methods. Finally, they extend their metric to evaluate individual sample quality, enabling quality assessment of interpolations and providing insights into the shape of the latent space that produces realistic images.</td>