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Hi,is there any guarantee not only a few of embeddings being selected? #2

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jiqizaisikao opened this issue Jan 23, 2018 · 3 comments

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@jiqizaisikao
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jiqizaisikao commented Jan 23, 2018

Hi,i have run your source code,it seems that it works well,but i have one question,for the weights of the embeddings are inited randomly,and in tranning it always select the nearest neighbors,so is there any guarantee that not a few of embeddings always being selected?

@hiwonjoon
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hiwonjoon commented Jan 23, 2018

It's a good question. I do not have an answer though since I am not the authors of the paper.
In my humble opinion, describing in the conceptual level, embedding is also be trained to maximize interpretability of training examples, so it could become the most informative supports for a dataset.

@leao1995
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When I run the cifar example, it always selects a fixed subset of the embedding, so the issue indeed exists.

@bckim92
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bckim92 commented Apr 13, 2019

Kaiser et al. addressed this issue in their paper (https://arxiv.org/abs/1803.03382) and they called it index collapse.

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