the behavior of func generate_target

[zhanjw]

It is a bit difficult to understand the behavior of setting the target of normal samples in the training of holistic_head to 1 in generate_target,

def generate_target(self, target, eval=False):
        targets = list()
        if eval:
            targets.append(target==0)
            targets.append(target)
            targets.append(target)
            targets.append(target)
            return targets
        else:
            temp_t = target != 0
            targets.append(target == 0)
            targets.append(temp_t[target != 2])
            targets.append(temp_t[target != 1])
            targets.append(target != 0)
        return targets

Can you assist me in understanding it? There is no mention of this in the paper.

[CC2033625919]

It is a bit difficult to understand the behavior of setting the target of normal samples in the training of holistic_head to 1 in generate_target,

def generate_target(self, target, eval=False):
        targets = list()
        if eval:
            targets.append(target==0)
            targets.append(target)
            targets.append(target)
            targets.append(target)
            return targets
        else:
            temp_t = target != 0
            targets.append(target == 0)
            targets.append(temp_t[target != 2])
            targets.append(temp_t[target != 1])
            targets.append(target != 0)
        return targets

Can you assist me in understanding it? There is no mention of this in the paper.

I have the same problem about it. Do you understand it now? If it is, could you explain the problem? Thank you!

[caiyu6666]

I think the normality head predict the probability of normality, that's the reason why they minus this score to obatin final anomaly score.

[Choubo]

Hi, @zhanjw @CC2033625919

As @caiyu6666 said, we want holistic_head to predict the probability of normality, so we changed the target to learn the feature of normality.

Cheers, Choubo