Is supervised loss L^s applied to both source and target data?

[Hayoung93]

Hi, I read your paper very interestingly and currently following your code implementation.

Unlike classification branch which doesn't use target domain's label information, it seems that the code of detection branch is using target domain's ground truth bboxes. Is it intentional or did I miss something?

Thank you.

The cls branch:

MIC/cls/examples/cdan_mcc_sdat.py

Lines 206 to 207 in 1aa2f5e

	x_s, labels_s = next(train_source_iter)
	x_t, _ = next(train_target_iter)

At the det branch:

MIC/det/maskrcnn_benchmark/engine/trainer.py

Lines 137 to 145 in 1aa2f5e

	for iteration, ((source_images, source_targets, idx1), (target_images, target_targets, idx2)) in enumerate(zip(source_data_loader, target_data_loader), start_iter):
	data_time = time.time() - end
	arguments["iteration"] = iteration
	scheduler.step()
	images = (source_images+target_images).to(device)
	targets = [target.to(device) for target in list(source_targets+target_targets)]
	loss_dict = model(images, targets)

[krumo]

Hi, thanks for your interest! For domain adaptation detection, the target ground truth is loaded but not used in loss computation. Our strategy is to use a flag to filter out losses on target domain data when computing task losses on RPN and bbox head. To avoid confusion, we also use a pseudo label to replace the annotations of target domain when loading data.

[Hayoung93]

Thanks for the explanation! 😃