Why are the weights not shared in the backbone and shared in the classifier?

[Zachary Wang]

Why are the weights not shared in the backbone and shared in the classifier?

class two_view_net(nn.Module):
    def __init__(self, class_num, droprate, stride = 2, pool = 'avg', share_weight = False, VGG16=False):
        super(two_view_net, self).__init__()
        if VGG16:
            self.model_1 =  ft_net_VGG16(class_num, stride=stride, pool = pool)
        else:
            self.model_1 =  ft_net(class_num, stride=stride, pool = pool)
        if share_weight:
            self.model_2 = self.model_1
        else:
            if VGG16:
                self.model_2 =  ft_net_VGG16(class_num, stride = stride, pool = pool)
            else:
                self.model_2 =  ft_net(class_num, stride = stride, pool = pool)

        self.classifier = ClassBlock(2048, class_num, droprate)
        if pool =='avg+max':
            self.classifier = ClassBlock(4096, class_num, droprate)
        if VGG16:
            self.classifier = ClassBlock(512, class_num, droprate)
            if pool =='avg+max':
                self.classifier = ClassBlock(1024, class_num, droprate)

    def forward(self, x1, x2):
        if x1 is None:
            y1 = None
        else:
            x1 = self.model_1(x1)
            y1 = self.classifier(x1)

        if x2 is None:
            y2 = None
        else:
            x2 = self.model_2(x2)
            y2 = self.classifier(x2)
        return y1, y2

Weights is not shared in self.model_1 and self.model_1 ,but the classifier does share weight,why?

[Zhedong Zheng]

Hi @wpumain
The low level feature may be different style, e.g., illumination (You may check the satellite and drone images).

But we want to mapping them to one shared space, so we share the final classifier.