update: gayoon profile

MICV-yonsei · Jun 27, 2024 · 2a30bc5 · 2a30bc5
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diff --git a/author/gayoon-choi/avatar.png b/author/gayoon-choi/avatar.png
diff --git a/...tar_hubf4e0adba75a463511945aa8f4abbba6_119847_270x270_fill_lanczos_center_3.png b/...tar_hubf4e0adba75a463511945aa8f4abbba6_119847_270x270_fill_lanczos_center_3.png
diff --git a/index.html b/index.html
@@ -326,7 +326,7 @@ <h2><a href="/author/yumin-kim/">Yumin Kim</a></h2>
                    alt="Avatar"></a>
             <div class="portrait-title">
               <h2><a href="/author/gayoon-choi/">Gayoon Choi</a></h2>
-              <p class="people-interests">What&rsquo;s your interests?</p>
+              <p class="people-interests">Parameter Efficient Fine-Tuning, Image Editing, Test Time Domain Adaptation</p>
             </div>
           </div>
           <div class="col-12 col-sm-auto people-person"><a href="/author/jaehoon-joo/"><img

diff --git a/promptdistill2024/index.html b/promptdistill2024/index.html
@@ -268,7 +268,7 @@
 
 
 
-<meta name="description" content="&lt;!DOCTYPE html&gt; PromptDistillation PromptDistillation for Weight-free Transfer Learning Gayoon Choi, Yumin Kim, Seong Jae Hwang Yonsei University Paper arXiv Code We introduce PromptDistillation for weight-free transfer learning." />
+<meta name="description" content="&lt;!DOCTYPE html&gt; Prompt Distillation Prompt Distillation for Weight-free Transfer Learning Gayoon Choi, Yumin Kim, Seong Jae Hwang Yonsei University Paper arXiv Code We introduce Prompt Distillation for weight-free transfer learning." />
 
 
 
@@ -317,7 +317,7 @@
 <meta property="og:site_name" content="MICV" />
 <meta property="og:url" content="http://localhost:1313/promptdistill2024/" />
 <meta property="og:title" content="MICV" />
-<meta property="og:description" content="&lt;!DOCTYPE html&gt; PromptDistillation PromptDistillation for Weight-free Transfer Learning Gayoon Choi, Yumin Kim, Seong Jae Hwang Yonsei University Paper arXiv Code We introduce PromptDistillation for weight-free transfer learning." /><meta property="og:image" content="http://localhost:1313/media/icon_huc20aa2f6f5c0a6f78f1951b0621355e5_26767_512x512_fill_lanczos_center_3.png" /><meta property="og:locale" content="en-us" />
+<meta property="og:description" content="&lt;!DOCTYPE html&gt; Prompt Distillation Prompt Distillation for Weight-free Transfer Learning Gayoon Choi, Yumin Kim, Seong Jae Hwang Yonsei University Paper arXiv Code We introduce Prompt Distillation for weight-free transfer learning." /><meta property="og:image" content="http://localhost:1313/media/icon_huc20aa2f6f5c0a6f78f1951b0621355e5_26767_512x512_fill_lanczos_center_3.png" /><meta property="og:locale" content="en-us" />
 
 
 
@@ -799,7 +799,7 @@ <h1></h1>
         content="Deformable Neural Radiance Fields creates free-viewpoint portraits (nerfies) from casually captured videos.">
   <meta name="keywords" content="Nerfies, D-NeRF, NeRF">
   <meta name="viewport" content="width=device-width, initial-scale=1">
-  <title>PromptDistillation</title>
+  <title>Prompt Distillation</title>
 
   <!-- Global site tag (gtag.js) - Google Analytics -->
   <script async src="https://www.googletagmanager.com/gtag/js?id=G-PYVRSFMDRL"></script>
@@ -858,7 +858,7 @@ <h1></h1>
     <div class="container is-max-desktop">
       <div class="columns is-centered">
         <div class="column has-text-centered">
-          <h1 class="title is-2 publication-title">PromptDistillation for Weight-free Transfer Learning</h1>
+          <h1 class="title is-2 publication-title">Prompt Distillation for Weight-free Transfer Learning</h1>
 
 
             <div class="is-size-4 publication-authors">
@@ -887,7 +887,7 @@ <h1 class="title is-2 publication-title">PromptDistillation for Weight-free Tran
         <div class="publication-links">
           <!-- PDF Link. -->
           <span class="link-block">
-            <a href=""
+            <a href="https://drive.google.com/drive/folders/1bnxQ8Z5C5PkFtnZaMEZecdygcOolUKJW?usp=sharing"
                class="external-link button is-normal is-rounded is-dark">
               <span class="icon">
                   <i class="fas fa-file-pdf"></i>
@@ -896,7 +896,7 @@ <h1 class="title is-2 publication-title">PromptDistillation for Weight-free Tran
             </a>
           </span>
           <span class="link-block">
-            <a href=""
+            <a href="https://drive.google.com/drive/folders/1bnxQ8Z5C5PkFtnZaMEZecdygcOolUKJW?usp=sharing"
                class="external-link button is-normal is-rounded is-dark">
               <span class="icon">
                   <i class="ai ai-arxiv"></i>
@@ -906,7 +906,7 @@ <h1 class="title is-2 publication-title">PromptDistillation for Weight-free Tran
           </span>
           <!-- Code Link. -->
           <span class="link-block">
-            <a href=""
+            <a href="https://drive.google.com/drive/folders/1bnxQ8Z5C5PkFtnZaMEZecdygcOolUKJW?usp=sharing"
                class="external-link button is-normal is-rounded is-dark">
               <span class="icon">
                   <i class="fab fa-github"></i>
@@ -924,11 +924,11 @@ <h1 class="title is-2 publication-title">PromptDistillation for Weight-free Tran
   <div class="container is-max-desktop">
     <div class="hero-body">
       <div class="image-at-center">
-        <img src="./static/images_promptdistill/1st.png" alt="Visualizing PromptDistillation and Baselines" width="80%">
+        <img src="./static/images_promptdistill/1st.png" alt="Visualizing Prompt Distillation and Baselines" width="80%">
       </div>
       <br>
       <h2 class="subtitle has-text-centered">
-        We introduce <b><i>PromptDistillation</i></b>,
+        We introduce <b><i>Prompt Distillation</i></b>,
         <br>reveal relationships in knowledge with visual prompts for a transfer learning.
       </h2>
     </div>
@@ -983,7 +983,7 @@ <h4 class="title is-4 has-text-centered">Pipeline</h4>
           <img src="./static/images_promptdistill/mainfigure.png" alt="Main figure">
           <br>
           <p>
-            The pipeline of <i>PromptDistillation</i> based transfer learning. 
+            The pipeline of <i>Prompt Distillation</i> based transfer learning. 
             In the pretraining step, a network is trained with a large-scale dataset and acquires generalization. 
             In the prompt distillation step, we inject prompts in the pretrained network's embedding space and compress knowledge from the pretrained networks into prompts. 
             Compression is conducted by training the network in supervision for simplicity, and nested dropout and knowledge distillation techniques can be applied. 
@@ -1093,7 +1093,7 @@ <h4 class="title is-4 has-text-centered">The Number of Prompt and Distillation E
     <h2 class="title">BibTeX</h2>
     <pre><code>@article{promptdistill2024,
     author    = {Choi, Gayoon and Kim, Yumin and Hwang, Seong Jae},
-    title     = {PromptDistillation for Weight-free Transfer Learning},
+    title     = {Prompt Distillation for Weight-free Transfer Learning},
     month     = {July},
     year      = {2024},
 }</code></pre>

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