equation

index.html

@@ -39,7 +39,7 @@
         e.preventDefault();
         if (!$(this).hasClass('selected')) {
 
-            $('.adaptive-loss-formula-content > .formula').hide(200);
+            $('.formula-content').hide(200);
             $('.formula-list > a').removeClass('selected');
             $(this).addClass('selected');
             var target = $(this).attr('href');
@@ -420,8 +420,8 @@
       <div class="column container formula">
         <p>
           Attackers can design adaptive attacks to try to bypass BEYOND when the attacker knows all the parameters of the model 
-          and the detection strategy. For an SSL model with a feature extractor $$f$$, a projector $h$, and a classification head $g$, 
-          the classification branch can be formulated as $$\mathbb{C} = f\circ g$$ and the representation branch as $$\mathbb{R} = f\circ h$$. 
+          and the detection strategy. For an SSL model with a feature extractor `f`, a projector `h`, and a classification head `g`, 
+          the classification branch can be formulated as `\mathbb{C} = f\circ g` and the representation branch as `\mathbb{R} = f\circ h`. 
           To attack effectively, the adversary must deceive the target model while guaranteeing the label consistency and representation similarity of the SSL model. 
       </div>
     </div>
@@ -436,19 +436,19 @@
             <div style="clear: both"></div>
           </div>
           <div class="row align-items-center adaptive-loss-formula-content>
-            <span class="formula label-loss" style="">
+            <span class="formula label-loss formula-content" style="">
               $$
               \displaystyle 
               Loss_{label} = \frac{1}{k} \sum_{i=1}^{k} \mathcal{L}\left(\mathbb{C}\left(W^i(x+\delta)  \right), y_t\right)
               $$
             </span>
-            <span class="formula representation-loss" style="display: none;">
+            <span class="formula representation-loss formula-content" style="display: none;">
               $$
               \displaystyle
               Loss_{repre} = \frac{1}{k} \sum_{i=1}^{k}\mathcal{S}(\mathbb{R}(W^i(x+\delta)), \mathbb{R}(x+\delta))
               $$
             </span>
-            <span class="formula total-loss" style="display: none;">
+            <span class="formula total-loss formula-content" style="display: none;">
               $$\displaystyle \mathcal{L}_C(x+\delta, y_t) + Loss_{label} - \alpha \cdot Loss_{repre}$$
             </span>
           </div>
@@ -458,15 +458,15 @@
 
     <div class="columns is-centered">
       <div class="column container adaptive-loss-formula-content">
-        <p class="formula label-loss">
-          where $$k$$ represents the number of generated neighbors, $$y_t$$ is the target class, and $$\mathcal{L}$$ is the cross entropy loss function.
+        <p class="formula label-loss formula-content">
+          where `k` represents the number of generated neighbors, `y_t` is the target class, and `\mathcal{L}` is the cross entropy loss function.
         </p>
-        <p class="formula representation-loss" style="display: none">
-          where $$\mathcal{S}$$ is the cosine similarity.
+        <p class="formula representation-loss formula-content" style="display: none">
+          where `k` represents the number of generated neighbors, and `mathcal{S}` is the cosine similarity.
         </p>
           
-        <p class="formula total-loss" style="display: none;">
-          where $$\mathcal{L}_C$$ indicates classifier's loss function, $y_t$ is the targeted class, and $\alpha$ refers to a hyperparameter.
+        <p class="formula total-loss formula-content" style="display: none;">
+          where `\mathcal{L}_C` indicates classifier's loss function, `y_t` is the targeted class, and `\alpha` refers to a hyperparameter.
         </p>
       </div>
     </div>