Needs explanation about the train and test results

[anwaar0]

Currently, I am using this library, I found these test results during testing for MNIST-USPS digit domain adaptation.

TEST RESULTS
{'Te_domain_acc': 0.29888888888888887,
'Te_source_acc': 0.9421212121212121,
'Te_target_acc': 0.1322222222222222,
'avg_test_loss': 1.5861893892288208,
'test_loss': 1.5861893892288208}

Can you explain the above short form of evaluation metrics? What I understood is
Te_domain_acc mean test domain accuracy
Te_source_acc mean test source accuracy
Te_target_acc mean test target accuracy
avg_test_loss mean average test loss
test_loss mean test loss

can you briefly describe the above, especially about Te_domain_acc in terms of MNIST-USPS?

Further, I also need a description of the table given below. I got this table after the completion of training.
method source acc target acc domain acc
CDAN 98.5% +- 0.25 90.1% +- 1.70 42.3% +- 2.63 (Validation)
CDAN 98.5% +- 0.24 90.6% +- 1.78 41.8% +- 2.56 (Test)
CDAN-E 98.6% +- 0.14 92.0% +- 0.79 33.8% +- 6.07 (Validation)
CDAN-E 98.6% +- 0.18 92.5% +- 1.34 34.0% +- 5.52 (Test)
DAN 98.8% +- 0.14 95.1% +- 0.58 nan% +- nan (Validation)
DAN 98.9% +- 0.13 95.5% +- 0.40 nan% +- nan (Test)
DANN 98.9% +- 0.19 93.1% +- 1.49 46.9% +- 18.23 (Validation)
DANN 98.9% +- 0.18 93.6% +- 0.89 46.3% +- 18.16 (Test)
JAN 98.6% +- 0.10 91.7% +- 0.97 nan% +- nan (Validation)
JAN 98.6% +- 0.17 91.7% +- 0.87 nan% +- nan (Test)
Source 98.9% +- 0.12 93.5% +- 1.41 72.4% +- 11.27 (Validation)
Source 98.9% +- 0.17 94.0% +- 0.75 71.6% +- 11.04 (Test)
WDGRL 97.2% +- 0.73 74.7% +- 7.56 54.9% +- 22.12 (Validation)
WDGRL 97.1% +- 0.74 73.7% +- 7.91 54.7% +- 21.84 (Test)

I understand that different methods are applied to source and target datasets. However, I still have a few questions,
Is this library using the pre-trained weights of MINIST (source)?
What is 42.3% +_2.63 in the domain ACC (first row and first column )?

I know, my issue may be general but it will be great if someone can provide information about these results.
Thank you.