how to reproduce office-31's W->A result in paper

[sunbaigui]

I've tried to reproduce the result of office-31, but there's no good. I only got 74.6 in W->A, which is far away from 76.8 as claimed in the paper. Could you please kindly help me to see if there's any trick I've missed.

[ly19965]

Similar un-reproduced result is occurred in the A->W of office-31. We achieve only 94.088 performance, which is far away from 96.1 reported in your paper. Could you please kindly help me to see if there's any trick I've missed. Log is as follows:

arch = resnet50
gpu_id = 6
dset = office
s_dset_path = data/list/office/amazon_31.txt
t_dset_path = data/list/office/webcam_31.txt
output_dir = log/office31
workers = 4
epochs = 80
iters_per_epoch = 500
print_freq = 100
batch_size = 32
lr = 0.01
momentum = 0.9
weight_decay = 0.001
seed = 1
lambda0 = 0.25
MI = 0.1

epoch = 00, best_acc1 = 86.038, best_acc1 = 86.038
epoch = 01, best_acc1 = 90.314, best_acc1 = 90.314
epoch = 02, best_acc1 = 91.447, best_acc1 = 91.447
epoch = 03, best_acc1 = 92.075, best_acc1 = 92.075
epoch = 04, best_acc1 = 92.075, best_acc1 = 92.075
epoch = 05, best_acc1 = 92.704, best_acc1 = 92.704
epoch = 06, best_acc1 = 93.333, best_acc1 = 93.333
epoch = 07, best_acc1 = 93.711, best_acc1 = 93.711
epoch = 08, best_acc1 = 93.711, best_acc1 = 93.711
epoch = 09, best_acc1 = 93.333, best_acc1 = 93.711
epoch = 10, best_acc1 = 93.836, best_acc1 = 93.836
epoch = 11, best_acc1 = 93.333, best_acc1 = 93.836
epoch = 12, best_acc1 = 93.459, best_acc1 = 93.836
epoch = 13, best_acc1 = 93.585, best_acc1 = 93.836
epoch = 14, best_acc1 = 94.088, best_acc1 = 94.088
epoch = 15, best_acc1 = 93.333, best_acc1 = 94.088
epoch = 16, best_acc1 = 93.333, best_acc1 = 94.088
epoch = 17, best_acc1 = 93.208, best_acc1 = 94.088
epoch = 18, best_acc1 = 92.956, best_acc1 = 94.088
epoch = 19, best_acc1 = 92.579, best_acc1 = 94.088
epoch = 20, best_acc1 = 92.956, best_acc1 = 94.088
epoch = 21, best_acc1 = 92.956, best_acc1 = 94.088
epoch = 22, best_acc1 = 93.082, best_acc1 = 94.088
epoch = 23, best_acc1 = 92.830, best_acc1 = 94.088
epoch = 24, best_acc1 = 92.956, best_acc1 = 94.088
epoch = 25, best_acc1 = 92.453, best_acc1 = 94.088
epoch = 26, best_acc1 = 92.579, best_acc1 = 94.088
epoch = 27, best_acc1 = 92.579, best_acc1 = 94.088
epoch = 28, best_acc1 = 92.579, best_acc1 = 94.088
epoch = 29, best_acc1 = 92.830, best_acc1 = 94.088
epoch = 30, best_acc1 = 92.830, best_acc1 = 94.088
epoch = 31, best_acc1 = 92.830, best_acc1 = 94.088
epoch = 32, best_acc1 = 92.830, best_acc1 = 94.088
epoch = 33, best_acc1 = 92.956, best_acc1 = 94.088
epoch = 34, best_acc1 = 92.830, best_acc1 = 94.088
epoch = 35, best_acc1 = 92.704, best_acc1 = 94.088
epoch = 36, best_acc1 = 92.704, best_acc1 = 94.088
epoch = 37, best_acc1 = 92.453, best_acc1 = 94.088
epoch = 38, best_acc1 = 92.956, best_acc1 = 94.088
epoch = 39, best_acc1 = 92.453, best_acc1 = 94.088
epoch = 40, best_acc1 = 93.208, best_acc1 = 94.088

[Xiemixue]

It seems that you are trying to reproduce the results of "BSP+TSA". The BSP in our paper is short for BSP+CDAN. We run "BSP+TSA" based on the open code at https://github.com/thuml/Transfer-Learning-Library/tree/master/examples/domain_adaptation/classification/cdan.py. You can add BSP loss and our TSA loss on the basis.

[sunbaigui]

It seems that you are trying to reproduce the results of "BSP+TSA". The BSP in our paper is short for BSP+CDAN. We run "BSP+TSA" based on the open code at https://github.com/thuml/Transfer-Learning-Library/tree/master/examples/domain_adaptation/classification/cdan.py. You can add BSP loss and our TSA loss on the basis.

followed your suggestion, still can not reproduce， could you please release the code for this task ?