- This repository contains code for our paper HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation Download paper here
- If you have any question about our paper or code, please don't hesitate to contact with me ahucomputer@126.com, we will update our repository accordingly
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Dataset The code as well as the dataset can be downloaded here HoMM in MNIST and HoMM in Office&Office-Home
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requirements Python==2.7, tensorflow==1.9, opencv
- MNIST You can run TrainLenet.py in HoMM-mnist.
- Office&Office-Home You can run finetune.py in HoMM_office/resnet/.
- We have provide four functions HoMM3, HoMM4, HoMM and KHoMM conresponding to the third-order HoMM, fourth-order HoMM, Arbitrary-order moment matching, and Kernel HoMM.
- Readers can reimplement the HoMM in their work very easily by using the following function.
- In our code, xs and xt denotes source and target deep features in the adapted layer. the dimension of xs and xt is b*L where b is the batchsize and L is the number of neurons in the adapted layer. num denotes the N in our paper, which indicates the number of sampled values in the high-level tensor.
- It is worth noting that the relu activation function can not be applied to the adapted layer, as relu activation function will make most of the values in the high-level tensor to be zero, which will make our HoMM fail. Therefore, we adopt tanh activation function in the adapted layer
HoMM3
def HoMM3_loss(self, xs, xt):
xs = xs - tf.reduce_mean(xs, axis=0)
xt = xt - tf.reduce_mean(xt, axis=0)
xs=tf.expand_dims(xs,axis=-1)
xs = tf.expand_dims(xs, axis=-1)
xt = tf.expand_dims(xt, axis=-1)
xt = tf.expand_dims(xt, axis=-1)
xs_1=tf.transpose(xs,[0,2,1,3])
xs_2 = tf.transpose(xs, [0, 2, 3, 1])
xt_1 = tf.transpose(xt, [0, 2, 1, 3])
xt_2 = tf.transpose(xt, [0, 2, 3, 1])
HR_Xs=xs*xs_1*xs_2 # dim: b*L*L*L
HR_Xs=tf.reduce_mean(HR_Xs,axis=0) #dim: L*L*L
HR_Xt = xt * xt_1 * xt_2
HR_Xt = tf.reduce_mean(HR_Xt, axis=0)
return tf.reduce_mean(tf.square(tf.subtract(HR_Xs, HR_Xt)))- HoMM4
- The adapted layer has 90 neurons, we divided them into 3 group with each group 30 neurons.
def HoMM4(self,xs,xt):
ind=tf.range(tf.cast(xs.shape[1],tf.int32))
ind=tf.random_shuffle(ind)
xs=tf.transpose(xs,[1,0])
xs=tf.gather(xs,ind)
xs = tf.transpose(xs, [1, 0])
xt = tf.transpose(xt, [1, 0])
xt = tf.gather(xt, ind)
xt = tf.transpose(xt, [1, 0])
return self.HoMM4_loss(xs[:,:30],xt[:,:30])+self.HoMM4_loss(xs[:,30:60],xt[:,30:60])+self.HoMM4_loss(xs[:,60:90],xt[:,60:90])
def HoMM4_loss(self, xs, xt):
xs = xs - tf.reduce_mean(xs, axis=0)
xt = xt - tf.reduce_mean(xt, axis=0)
xs = tf.expand_dims(xs,axis=-1)
xs = tf.expand_dims(xs, axis=-1)
xs = tf.expand_dims(xs, axis=-1)
xt = tf.expand_dims(xt, axis=-1)
xt = tf.expand_dims(xt, axis=-1)
xt = tf.expand_dims(xt, axis=-1)
xs_1 = tf.transpose(xs,[0,2,1,3,4])
xs_2 = tf.transpose(xs, [0, 2, 3, 1,4])
xs_3 = tf.transpose(xs, [0, 2, 3, 4, 1])
xt_1 = tf.transpose(xt, [0, 2, 1, 3,4])
xt_2 = tf.transpose(xt, [0, 2, 3, 1,4])
xt_3 = tf.transpose(xt, [0, 2, 3, 4, 1])
HR_Xs=xs*xs_1*xs_2*xs_3 # dim: b*L*L*L*L
HR_Xs=tf.reduce_mean(HR_Xs,axis=0) # dim: L*L*L*L
HR_Xt = xt * xt_1 * xt_2*xt_3
HR_Xt = tf.reduce_mean(HR_Xt, axis=0)
return tf.reduce_mean(tf.square(tf.subtract(HR_Xs, HR_Xt)))- Arbitrary-order Moment Matching
def HoMM(self,xs, xt, order=3, num=300000):
xs = xs - tf.reduce_mean(xs, axis=0)
xt = xt - tf.reduce_mean(xt, axis=0)
dim = tf.cast(xs.shape[1], tf.int32)
index = tf.random_uniform(shape=(num, dim), minval=0, maxval=dim - 1, dtype=tf.int32)
index = index[:, :order]
xs = tf.transpose(xs)
xs = tf.gather(xs, index) ##dim=[num,order,batchsize]
xt = tf.transpose(xt)
xt = tf.gather(xt, index)
HO_Xs = tf.reduce_prod(xs, axis=1)
HO_Xs = tf.reduce_mean(HO_Xs, axis=1)
HO_Xt = tf.reduce_prod(xt, axis=1)
HO_Xt = tf.reduce_mean(HO_Xt, axis=1)
return tf.reduce_mean(tf.square(tf.subtract(HO_Xs, HO_Xt)))
- If you find it helpful for you, please cite our paper
@inproceedings{chen2020HoMM,
title={HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation},
author={Chao Chen, Zhihang Fu, Zhihong Chen, Sheng Jin, Zhaowei Cheng, Xinyu Jin, Xian-Sheng Hua},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={34},
year={2020}
}