Why was faiss not used for DC2?

[yutaizhou]

Hello,

Absolutely fascinating work! I was looking into how DC2 improves upon the original DC, and noticed that DC2 implementation does not use faiss for clustering. May I know why this choice was made?

Thank you.

[mathildecaron31]

Hello @yutaizhou

I implemented k-means clustering with pytorch directly in this work because I found it simpler to avoid an additionnal dependency.

I think you can implement DC2 with faiss as well, it should not make any difference performance wise :)

[yutaizhou]

Ah yeah, getting faiss to install and interoperate with torch tensors (on both GPU and CPU) have not been straight forward, but that is the approach I am currently taking. Thanks!

[yutaizhou]

One more question, are you aware of any application of DeepClustering, DC2, or SwAV, applied to time series data? I am trying to implement DC2 with a transformer backbone for time series data, and would love to see what others have done :)

[mathildecaron31]

Mm no sorry I cannot think of any such work :/

[yutaizhou]

Sorry I have yet another question that I was hoping you could answer. In section D.1 of the paper, the cross entropy loss between the pseudo label q and the classification score z@c is minimized with respect to z. This means only the backbone architecture, i.e., resnet, is updated, not the classification head itself, which contains the centroids learned from k-means.

Centroids are supposed to be updated once per epoch from k-means, and backbone is supposed to be updated with every mini-batch. In your code, however, you call detach() on the output of the backbone, not the classification head. Wouldn't this flow the gradients only to the centroids, and not the parameters of the backbone?

Or am I not understanding the detach() operation correctly?

[BoPang1996]

Sorry I have yet another question that I was hoping you could answer. In section D.1 of the paper, the cross entropy loss between the pseudo label q and the classification score z@c is minimized with respect to z. This means only the backbone architecture, i.e., resnet, is updated, not the classification head itself, which contains the centroids learned from k-means.

Centroids are supposed to be updated once per epoch from k-means, and backbone is supposed to be updated with every mini-batch. In your code, however, you call detach() on the output of the backbone, not the classification head. Wouldn't this flow the gradients only to the centroids, and not the parameters of the backbone?

Or am I not understanding the detach() operation correctly?

I have the same question. Why the prototypes are updated every iteration.