Digits datasets
We use the same network architecture, number of epochs, batch_size for
all experiments. These are the results obtained with the default
configuration in digits_network.json. The feature extractor is a
convolutional neural network with 3 convolutional layers. The classifier
is a 3-layer fully connected neural network.
MNIST -> MNIST-M (5 runs)
| Method |
source acc |
target acc |
|---|
| Source |
89.0% +- 2.52 |
34.0% +- 1.71 |
| DANN |
94.2% +- 1.57 |
37.5% +- 2.85 |
| CDAN |
98.7% +- 0.19 |
68.4% +- 1.80 |
| CDAN-E |
98.7% +- 0.12 |
69.6% +- 1.51 |
| DAN |
98.0% +- 0.68 |
47.0% +- 1.85 |
| JAN |
96.4% +- 4.57 |
52.9% +- 2.16 |
| WDGRL |
93.9% +- 2.70 |
52.0% +- 4.82 |
MNIST -> USPS (5 runs)
| Method |
source acc |
target acc |
|---|
| Source |
99.2% +- 0.08 |
94.2% +- 1.07 |
| DANN |
99.1% +- 0.15 |
93.8% +- 1.06 |
| CDAN |
98.8% +- 0.17 |
90.7% +- 1.17 |
| CDAN-E |
98.9% +- 0.11 |
90.3% +- 0.98 |
| DAN |
99.0% +- 0.14 |
95.0% +- 0.83 |
| JAN |
98.6% +- 0.30 |
89.5% +- 2.00 |
| WDGRL |
98.7% +- 0.13 |
85.7% +- 6.57 |
SVHN -> MNIST (5 runs)
| Method |
source acc |
target acc |
|---|
| Source |
80.4% +- 1.65 |
60.2% +- 1.98 |
| DANN |
80.7% +- 2.09 |
61.7% +- 2.75 |
| CDAN |
80.5% +- 1.91 |
79.0% +- 3.13 |
| CDAN-E |
82.0% +- 0.74 |
77.9% +- 5.59 |
| DAN |
80.7% +- 1.26 |
54.8% +- 2.76 |
| JAN |
79.6% +- 0.67 |
57.9% +- 2.35 |
| WDGRL |
80.5% +- 1.09 |
59.5% +- 3.61 |
MNIST -> SVHN (5 runs)
This problem is much harder than the others and results are usually not
reported. Indeed, most methods fail to improve performance – on the
contrary, aligning features results in decreased global performance.
| Method |
source acc |
target acc |
|---|
| Source |
91.6% +- 2.28 |
16.4% +- 3.31 |
| DANN |
96.2% +- 0.24 |
19.5% +- 2.60 |
| CDAN |
67.0% +- 12.74 |
11.5% +- 1.62 |
| CDAN-E |
59.8% +- 18.99 |
11.3% +- 1.08 |
| DAN |
93.3% +- 3.94 |
16.7% +- 1.19 |
| JAN |
68.4% +- 12.72 |
11.5% +- 1.53 |
| WDGRL |
77.4% +- 3.11 |
13.8% +- 1.75 |
Office 31 dataset
We use the same network architecture, number of epochs, batch_size for
all experiments. These are the results obtained with the default
configuration in office31_network.json. The feature extractor is a
ResNet50 with the last layer removed. The task classifier is linear.
The results on the source look a bit lower than results reported in the
literature (which I found from 68 to 80%). Note, however, that the
parameters haven’t been full finetuned, and that the confidence
intervals are wider than those reported in the literature.
Amazon to Webcam (5 runs)
| Method |
source acc |
target acc |
|---|
| Source |
83.0% +- 1.04 |
59.7% +- 3.67 |
| DANN |
82.7% +- 1.56 |
73.4% +- 5.63 |
| CDAN |
82.0% +- 1.30 |
82.3% +- 3.52 |
| CDAN-E |
82.7% +- 0.74 |
81.6% +- 4.67 |
| DAN |
83.0% +- 1.65 |
68.1% +- 1.96 |
| JAN |
82.0% +- 2.02 |
64.6% +- 3.70 |
| WDGRL |
83.4% +- 1.72 |
75.5% +- 3.13 |
Amazon to DSLR (5 runs)
| Method |
source acc |
target acc |
|---|
| Source |
81.9% +- 1.02 |
62.7% +- 3.25 |
| DANN |
80.2% +- 0.99 |
68.8% +- 2.78 |
| CDAN |
81.2% +- 2.06 |
72.0% +- 2.09 |
| CDAN-E |
80.1% +- 1.35 |
70.9% +- 3.24 |
| DAN |
82.3% +- 2.35 |
68.7% +- 1.36 |
| JAN |
78.1% +- 4.77 |
62.3% +- 2.39 |
| WDGRL |
79.6% +- 2.53 |
68.8% +- 1.63 |
DSLR to Amazon (5 runs)
| Method |
source acc |
target acc |
|---|
| Source |
95.6% +- 2.08 |
55.6% +- 6.44 |
| DANN |
91.6% +- 2.42 |
53.8% +- 5.61 |
| CDAN |
87.2% +- 3.09 |
56.6% +- 4.87 |
| CDAN-E |
89.1% +- 2.09 |
54.1% +- 6.71 |
| DAN |
95.9% +- 2.05 |
58.1% +- 5.49 |
| JAN |
92.8% +- 2.75 |
54.1% +- 5.92 |
| WDGRL |
88.8% +- 2.08 |
56.6% +- 4.03 |