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Implementation of Domain Adaption in One-Shot Learning
Hi, I'd like to ask some question about the paper and the code:
The paper says in section 3.4 that
If the target query image can be correctly classified, the target image is "close" to the corresponding image in the projected feature space.
Does it mean when we train ADA+RSS, we would use the target domain label? If so, I think it's a little unfair to compare to the baseline such as PN+ADDA. Did I misunderstand something?
EDIT at 6/19: For question 1, I finally realized that the proposed method did not use the target domain label, I misunderstood the paper, sorry about that.
Is there typo in the train_rss.py? It seems that train_query_image
and train_query_label
are mismatched, maybe the first one should be corrected?
https://github.com/leonndong/DAOSL/blob/master/train_rss.py#L215
feed_dict={
train_support_images: x_support_set,
train_support_labels: y_support_set,
train_query_image: val_x_query, # is this typo?
train_query_label: y_query,
val_support_images: val_x_support_set,
val_support_labels: val_y_support_set, # also, I think these rows are redundant?
val_query_image: val_x_query, # also, I think these rows are redundant?
val_query_label: val_y_query # also, I think these rows are redundant?
})
EDIT: For question 2, I think it's not typo in that line. It matches the paper's approach.
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