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View Code? Open in Web Editor NEWCode for ICLR 2019 Paper, "MAX-MIG: AN INFORMATION THEORETIC APPROACH FOR JOINT LEARNING FROM CROWDS"
Code for ICLR 2019 Paper, "MAX-MIG: AN INFORMATION THEORETIC APPROACH FOR JOINT LEARNING FROM CROWDS"
if train:
path = './prepared/'
data_train_vgg16 = load_data(path + "data_train_vgg16.npy").transpose(0, 3, 1, 2)
I found your code includes a training.csv file in dogvscat dataset, but I didn't find it in the kaggle version.
I found that in the model of DoctorNet for CIFAR10, you don't use self.classifier in forward function. But for other datasets, you use it. Is this correct or just missing?
class left_neural_net_dn(nn.Module):
"""
left neural net for Docter Net method
"""
def __init__(self):
super(left_neural_net_dn, self).__init__()
for i in range(Config.expert_num):
m_name = "dn" + str(i+1)
self.add_module(m_name,nn.Linear(512, Config.num_classes))
self.vgg = VGG('VGG16')
self.features = self.vgg.features
self.classifier = self.vgg.classifier
def forward(self, x):
x = self.features(x)
x = x.view(-1, 512)
out = F.log_softmax(self.dn1(x),1).unsqueeze(0)
for name, module in self.named_children():
if name[0:2] != 'dn' or name == 'dn1':
continue
out = torch.cat((out,F.log_softmax(module(x), 1).unsqueeze(0)),0)
return out
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