relation

index.html

@@ -230,12 +230,21 @@
       </div>
     </div>
     <div class="columns is-centered">
-      <div class="column has-text-justified">
+      <div class="column has-text-justified is-four-fifths">
         <p>
           <strong>Figure 1. Neighborhood Relations of Benign Examples and AEs.</strong>
         </p>
       </div>
     </div>
+    <div class="columns is-centered">
+      <div class="column has-text-justified">
+        <p>
+          Latent Neighborhood Graph (LNG) represents the relationship between the input sample and the reference sample as a graph, 
+          whose nodes are embeddings extracted by DDN and edges are built according to distances between the input node and reference nodes, 
+          and train a graph neural network to detect AEs.
+        </p>
+      </div>
+    </div>
   </div>
 </section>
 
@@ -245,7 +254,7 @@
     <div class="columns is-centered">
       <div class="column container-centered">
         <img src="./static/images/overview.png" alt="Method Overview of BEYOND"/>
-        <p><strong>Figure 2. Overview of BEYOND.</strong>. First, we augment the input image to obtain a bunch of its neighbors. Then, we
+        <p><strong>Figure 2. Overview of BEYOND.</strong> First, we augment the input image to obtain a bunch of its neighbors. Then, we
           perform the label consistency detection mechanism on the classifier’s prediction of the input image and that of neighbors predicted by
           SSL’s classification head. Meanwhile, the representation similarity mechanism employs cosine distance to measure the similarity among
           the input image and its neighbors. Finally, The input image with poor label consistency or representation similarity is flagged as AE.</p>