text: The analyses of this paper (1) increasing the feature norm of correctly-classified examples induce smaller training loss, (2) increasing the feature norm of mis-classified examples upweight the contribution from hard examples, are interesting.  The reciprocal norm loss seems to be reasonable idea to improve the CNN learning based on the analyses.  However, the presentation of this paper need to be largely improved.  For example, Figure 3 seems to be not relevant to Property2  and may be show the feature norm is lower when the samples is hard example.  Therefore, the author used reciprocal norm loss which increases feature norm as shown in Figure 4.  However, both Figures are not explained in the main text,  and thus hard to understand the relation of Figure 3 and 4.  The author should refer all Figures and Tables.  Other issues are: -Large-margin Soft max in Figure 2 is not explained in the introduction section.  -In Eq.(7), P_j^I is not defined.  - In the Property3, The author wrote “ where r is lower bound of feature norm”.   However, r is not used. -In the experimental results, “RN” is not defined. -In the Table3, the order of \lambda should be increasing or decreasing order.  - Table 5 is not referred in the main text.