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@@ -228,7 +228,7 @@ <h2 class="title is-3">Overview</h2>
               <b>Dataset Distillation</b> aims at synthesizing a small synthetic dataset such that a model trained on
               this synthetic set will perform <b>equally well</b> as a model trained on the full, real dataset.
               Until now, no Dataset Distillation method has reached this completely lossless goal, in part due to the
-              fact that previous methods only remain effective when the size of synthetic dataset is <b>extremely
+              fact that previous methods only remain effective when the size of the synthetic dataset is <b>extremely
                 small</b>.
             </p>
             <p>
@@ -305,14 +305,14 @@ <h2 class="title is-3">Visualization</h2>
               What do <b>easy</b> patterns and <b>hard</b> patterns look like?
             </p>
             <p>
-              Here we visualize the images synthesized by matching <b>early</b> and <b>late</b> trajectories, where <b>easy</b> patterns and <b>hard</b> patterns are embedded respectively.
+              Here we visualize the images synthesized by matching <b>early</b> and <b>late</b> trajectories, where <b>easy</b> patterns and <b>hard</b> patterns are embedded into the synthetic data respectively.
 
             </p>
             <p>
-              As can be observed, matching <b>early</b> trajectories will blend the target object into the background and blur the details, which help DNNs to learn to identify commen (<b>easy</b>) samples according to their basic patterns.
+              As can be observed, matching <b>early</b> trajectories will blend the target object into the background and blur the details, which helps DNNs to learn to identify common (<b>easy</b>) samples according to their basic patterns.
             </p>
             <p>
-              Conversely, matching <b>late</b> trajectories will give more detials about the target, which help DNNs to learn to identify the outlier (<b>hard</b>) samples.
+              Conversely, matching <b>late</b> trajectories will give more details about the target, which helps DNNs to learn to identify the outlier (<b>hard</b>) samples.
             </p>
             <p>
 
@@ -399,4 +399,4 @@ <h2 class="title">BibTeX</h2>
 
 </body>
 
-</html>
+</html>