Deep Transfer Learning on PyTorch

This is a PyTorch library for deep transfer learning. We divide the code into two aspects: Single-source Unsupervised Domain Adaptation (SUDA) and Multi-source Unsupervised Domain Adaptation (MUDA). There are many SUDA methods, however I find there is a few MUDA methods with deep learning. Besides, MUDA with deep learning might be a more promising direction for domain adaptation.

Here I have implemented some deep transfer methods as follows:

• UDA
  • DDC：Deep Domain Confusion Maximizing for Domain Invariance
  • DAN: Learning Transferable Features with Deep Adaptation Networks (ICML2015)
  • Deep Coral: Deep CORAL Correlation Alignment for Deep Domain Adaptation (ECCV2016)
  • Revgrad: Unsupervised Domain Adaptation by Backpropagation (ICML2015)
• MUDA
  • Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple Sources (AAAI2019)

Results on Office31(UDA)

Method	A - W	D - W	W - D	A - D	D - A	W - A	Average
ResNet	68.4±0.5	96.7±0.5	99.3±0.1	68.9±0.2	62.5±0.3	60.7±0.3	76.1
DDC	75.8±0.2	95.0±0.2	98.2±0.1	77.5±0.3	67.4±0.4	64.0±0.5	79.7
DDC*	78.3±0.4	97.1±0.1	100.0±0.0	81.7±0.9	65.2±0.6	65.1±0.4	81.2
DAN	83.8±0.4	96.8±0.2	99.5±0.1	78.4±0.2	66.7±0.3	62.7±0.2	81.3
DAN*	82.6±0.7	97.7±0.1	100.0±0.0	83.1±0.9	66.8±0.3	66.6±0.4	82.8
DCORAL*	79.0±0.5	98.0±0.2	100.0±0.0	82.7±0.1	65.3±0.3	64.5±0.3	81.6
Revgrad	82.0±0.4	96.9±0.2	99.1±0.1	79.7±0.4	68.2±0.4	67.4±0.5	82.2
Revgrad*	82.6±0.9	97.8±0.2	100.0±0.0	83.3±0.9	66.8±0.1	66.1±0.5	82.8

Note that the results without '*' comes from paper. The results with '*' are run by myself with the code.

Results on Office31(MUDA)

Standards	Method	A,W - D	A,D - W	D,W - A	Average
	ResNet	99.3	96.7	62.5	86.2
	DAN	99.5	96.8	66.7	87.7
Single Best	DCORAL	99.7	98.0	65.3	87.7
	RevGrad	99.1	96.9	68.2	88.1
	DAN	99.6	97.8	67.6	88.3
Source Combine	DCORAL	99.3	98.0	67.1	88.1
	RevGrad	99.7	98.1	67.6	88.5
Multi-Source	MFSAN	99.5	98.5	72.7	90.2

Results on OfficeHome(MUDA)

Standards	Method	C,P,R - A	A,P,R - C	A,C,R - P	A,C,P - R	Average
	ResNet	65.3	49.6	79.7	75.4	67.5
	DAN	64.1	50.8	78.2	75.0	67.0
Single Best	DCORAL	68.2	56.5	80.3	75.9	70.2
	RevGrad	67.9	55.9	80.4	75.8	70.0
	DAN	68.5	59.4	79.0	82.5	72.4
Source Combine	DCORAL	68.1	58.6	79.5	82.7	72.2
	RevGrad	68.4	59.1	79.5	82.7	72.4
Multi-Source	MFSAN	72.1	62.0	80.3	81.8	74.1

Note that (1) Source combine: all source domains are combined together into a traditional single-source v.s. target setting. (2) Single best: among the multiple source domains, we report the best single source transfer results. (3) Multi-source: the results of MUDA methods.

Note

If you find that your accuracy is 100%, the problem might be the dataset folder. Please note that the folder structure required for the data provider to work is:

-dataset
    -amazon
    -webcam
    -dslr

Contact

If you have any problem about this library, please create an Issue or send us an Email at:

• zhuyongchun18s@ict.ac.cn
• jindongwang@outlook.com