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The paper DN-DETR presents a novel denoising training method to speed up DETR training, and they said that the bipartite graph matching algorithm we choose is unstable, and it may cause inconsistent optimization goals. So I think maybe we can adopt this idea into our model.
The text was updated successfully, but these errors were encountered:
If you fully understand the algorithm they use and the math behind it and you think it might improve the performance of our network, it may be a good option to pursue.
That said, in my view anything that increases the complexity of the current model should be delayed until after we have a more robust training resulting to models with relatively good performance.
The paper DN-DETR presents a novel denoising training method to speed up DETR training, and they said that the bipartite graph matching algorithm we choose is unstable, and it may cause inconsistent optimization goals. So I think maybe we can adopt this idea into our model.
The text was updated successfully, but these errors were encountered: