Issue on subspace dimension about dsn_fewshot

[chrysts]

Hi Tong Zhang,

Thanks for your interest in our work. Yes, you are correct that if we only have 1 shot and use exactly one embedded feature, the singular vector cannot be obtained.

In our code, you can see that when the number of shot is 1, we add an augmented sample (in total 2 samples).
Then we run svd with the number of column is two. This is also reflected in the Experiments section of our paper.
Hope this answers your question.

Cheers

[longspringcity]

Thanks for your prompt reply!

I'm still confused about some code details. In the 1-shot case, I printed out self.num_dim from Subspace_Projection.create_subspace() in Conv4/algorithm/subspace_projection.py and found that it is all 0. This causes the return value all_hyper_ planes to be []. Here is my code and the result of running it.

I noticed the use of inverted images to expand the support set at 1-shot, but the key problem is not setting the subspace dimension correctly when the Subspace_Projection class is initialized.

Thanks again for your time and have a great day!

[chrysts]

Hi Tong Zhang,

You can see in train_subspace_discriminative.py > line 61. The subspacedim is set to 1.
Yes, there is a mistake when I initialize the subspaceprojection class. I have edited the line to initialize subspace projection, you can pull or copy from train_subspace_discriminative.py. Thanks for spotting this out!