Partial Domain Adaptation¶
We provide benchmarks of different domain adaptation algorithms on Office-31 , Office-Home, VisDA-2017 and ImageNet-Caltech. Those domain adaptation algorithms includes:
Note
Originmeans the accuracy reported by the original paper.Avgis the accuracy reported by Transfer-Learn.Source Onlyrefers to the model trained with data from the source domain.
Note
We found that the accuracies of adversarial methods are not stable even after the random seed is fixed, thus we repeat running adversarial methods on Office-31 and VisDA-2017 for three times and report their average accuracy.
Office-31 accuracy on ResNet-50¶
Methods |
Origin |
Avg |
A → W |
D → W |
W → D |
A → D |
D → A |
W → A |
Source Only |
75.6 |
90.1 |
78.3 |
98.3 |
99.4 |
87.3 |
88.5 |
88.8 |
DANN |
43.4 |
82.4 |
60.0 |
94.9 |
98.1 |
71.3 |
84.9 |
85.0 |
PADA |
92.7 |
93.8 |
86.4 |
100.0 |
100.0 |
87.3 |
93.8 |
95.4 |
IWAN |
94.7 |
94.8 |
91.2 |
99.7 |
99.4 |
89.8 |
94.2 |
94.3 |
AFN |
/ |
93.1 |
87.8 |
95.6 |
99.4 |
87.9 |
93.9 |
94.1 |
Office-Home accuracy on ResNet-50¶
Methods |
Origin |
Avg |
Ar → Cl |
Ar → Pr |
Ar → Rw |
Cl → Ar |
Cl → Pr |
Cl → Rw |
Pr → Ar |
Pr → Cl |
Pr → Rw |
Rw → Ar |
Rw → Cl |
Rw → Pr |
Source Only |
53.7 |
60.1 |
42.0 |
66.9 |
78.5 |
56.4 |
55.2 |
65.4 |
57.9 |
36.0 |
75.5 |
68.7 |
43.6 |
74.8 |
DANN |
47.4 |
57.0 |
46.2 |
59.3 |
76.9 |
47.0 |
47.4 |
56.4 |
51.6 |
38.8 |
72.1 |
68.0 |
46.1 |
74.2 |
PADA |
62.1 |
65.9 |
52.9 |
69.3 |
82.8 |
59.0 |
57.5 |
66.4 |
66.0 |
41.7 |
82.5 |
78.0 |
50.2 |
84.1 |
IWAN |
63.6 |
71.3 |
59.2 |
76.6 |
84.0 |
67.8 |
66.7 |
69.2 |
73.3 |
55.0 |
83.9 |
79.0 |
58.3 |
82.2 |
AFN |
71.8 |
72.6 |
59.2 |
76.7 |
82.8 |
72.5 |
74.5 |
76.8 |
72.5 |
56.7 |
80.8 |
77.0 |
60.5 |
81.6 |
ImageNet-Caltech accuracy on ResNet-50¶
Methods |
Origin |
Avg |
I→C |
C→I |
Source Only |
68.9 |
73.3 |
71.8 |
74.8 |
DANN |
60.8 |
73.1 |
71.6 |
74.5 |
PADA |
72.8 |
79.2 |
79.2 |
79.1 |
IWAN |
75.7 |
78.9 |
77.5 |
75.7 |
VisDA-2017 accuracy on ResNet-50¶
Note that Origin means the accuracy reported by the original paper, Mean refers to the accuracy average over classes, while Avg refers to accuracy average over samples.
Methods |
Origin |
Mean |
plane |
bcycl |
bus |
car |
horse |
knife |
Avg |
Source Only |
45.3 |
50.9 |
59.2 |
31.3 |
68.7 |
73.2 |
69.3 |
3.4 |
60.0 |
DANN |
51.0 |
55.9 |
88.4 |
34.1 |
72.1 |
50.7 |
61.9 |
27.8 |
57.1 |
PADA |
53.5 |
60.5 |
89.4 |
35.1 |
72.5 |
69.2 |
86.7 |
10.1 |
66.8 |
IWAN |
/ |
61.5 |
89.2 |
57.0 |
61.5 |
55.2 |
80.1 |
25.7 |
66.8 |
AFN |
67.6 |
61.0 |
79.1 |
62.7 |
73.9 |
49.6 |
79.6 |
21.0 |
64.1 |
