equation
Browse files- index.html +4 -5
index.html
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@@ -40,7 +40,6 @@
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if (!$(this).hasClass('selected')) {
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$('.formula').hide(200);
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$('.eq-des').hide(200);
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$('.formula-list > a').removeClass('selected');
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$(this).addClass('selected');
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var target = $(this).attr('href');
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@@ -418,7 +417,7 @@
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<h2 class="title is-3">Adaptive Attack</h2>
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<div class="columns is-centered">
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<div class="column container">
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<p>
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Attackers can design adaptive attacks to try to bypass BEYOND when the attacker knows all the parameters of the model
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and the detection strategy. For an SSL model with a feature extractor <equation-inline>f</equation-inline>, a projector $h$, and a classification head $g$,
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@@ -459,14 +458,14 @@
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<div class="columns is-centered">
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<div class="column container">
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<p class="
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where $\displaystyle k$ represents the number of generated neighbors, $\displaystyle y_t$ is the target class, and $\displaystyle \mathcal{L}$ is the cross entropy loss function
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</p>
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<p class="
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where $\displaystyle \mathcal{S}$ is the cosine similarity.
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</p>
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<p class="
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where $\displaystyle \mathcal{L}_C$ indicates classifier's loss function, $\displaystyle y_t$ is the targeted class, and $\displaystyle \alpha$ refers to a hyperparameter.
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</p>
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</div>
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if (!$(this).hasClass('selected')) {
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$('.formula').hide(200);
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$('.formula-list > a').removeClass('selected');
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$(this).addClass('selected');
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var target = $(this).attr('href');
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<h2 class="title is-3">Adaptive Attack</h2>
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<div class="columns is-centered">
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<div class="column container formula">
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<p>
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Attackers can design adaptive attacks to try to bypass BEYOND when the attacker knows all the parameters of the model
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and the detection strategy. For an SSL model with a feature extractor <equation-inline>f</equation-inline>, a projector $h$, and a classification head $g$,
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<div class="columns is-centered">
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<div class="column container">
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<p class="formula label-loss">
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where $\displaystyle k$ represents the number of generated neighbors, $\displaystyle y_t$ is the target class, and $\displaystyle \mathcal{L}$ is the cross entropy loss function
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</p>
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<p class="formula representation-loss" style="display: none">
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where $\displaystyle \mathcal{S}$ is the cosine similarity.
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</p>
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<p class="formula total-loss" style="display: none;">
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where $\displaystyle \mathcal{L}_C$ indicates classifier's loss function, $\displaystyle y_t$ is the targeted class, and $\displaystyle \alpha$ refers to a hyperparameter.
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</p>
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</div>
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