Update reamde

Author: lizekai-richard

README.md

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-# <center>DD-Ranking</center>
+# <center>DD-Ranking: Rethinking the Evaluation of Dataset Distillation</center>
 
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@@ -40,7 +40,7 @@ Dataset Distillation (DD) aims to condense a large dataset into a much smaller o
 
 ![history](./static/history.png)
 
-Notebaly, more and more methods are transitting from "hard label" to "soft label" in dataset distillation, especially during evaluation. **Hard labels** are categorical, having the same format of the real dataset. **Soft labels** are distributions, typically generated by a pre-trained teacher model. 
+Notebaly, more and more methods are transitting from "hard label" to "soft label" in dataset distillation, especially during evaluation. **Hard labels** are categorical, having the same format of the real dataset. **Soft labels** are outputs of a pre-trained teacher model. 
 Recently, Deng et al., pointed out that "a label is worth a thousand images". They showed analytically that soft labels are exetremely useful for accuracy improvement. 
 
 However, since the essence of soft labels is **knowledge distillation**, we find that when applying the same evaluation method to randomly selected data, the test accuracy also improves significantly (see the figure above).