I don't get some point in line 3 of all 2 in this paper Towards Open set Deep Networks. Scores, I think, should be multiplied by (1-alpha_rankCDF) rather than (alpha_rankCDF)(which is the probability of being outlier) to be modified. It is because the newly added label should represent the addition of all probabilities of being outlier. As for simple example, if the probability of being outlier is zero(which means that this class should not be rejected),however, zero is multiplied by scores so that its score becomes zero. Thus, I think line in paper is mistake not line in code. Would you help me about this?
I don't get some point in line 3 of all 2 in this paper Towards Open set Deep Networks. Scores, I think, should be multiplied by (1-alpha_rankCDF) rather than (alpha_rankCDF)(which is the probability of being outlier) to be modified. It is because the newly added label should represent the addition of all probabilities of being outlier. As for simple example, if the probability of being outlier is zero(which means that this class should not be rejected),however, zero is multiplied by scores so that its score becomes zero. Thus, I think line in paper is mistake not line in code. Would you help me about this?