data argumentation

[zhangjinyangnwpu]

Hi, thanks for your code, it's elegant, and I learned a lot from it,
I have some questions when I read your paper,

1. I noticed that you do a lot of data argumentation when training, and I wonder how much this impacts the performance in semi-supervised learning?
2. In my research field, I can not do data argument for samples, and I just have a few like one or five samples per class, I wonder the keys and values define in memory could learn the semi-supervised, and how could we guarantee the memory updated with just very few labeled samples? think about this, in extra situation, we just have one sample, and I update the keys and values with this only sample, please asking your advice may this work?

Thank you.
Best wishes.

[yanbeic]

Hi,

Here are my understanding regarding your questions:

1. Data augmentation is beneficial and improves SSL in classification - avoid overfitting.
2. A very few samples (e.g. one sample) are unlikely to learn good representations for keys (i.e. class-level feature representations).