Comments (1)
@RenLibo-aircas thanks for your interest. Basically, the equipartition constraint (see Eq. 8) in Sinkhorn-Knopp iteration can improve the diversity of the cluster centers. But in practice, case-by-case consideration is needed. For example, if the patterns of the semantic classes of your interest are simple or the number of training data is limited, there is no need to adopt a large number of prototypes.
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Related Issues (20)
- When will the code be released?
- Question about the prototype initialization? HOT 2
- Question about code HOT 5
- parameter numbers of the entire model? HOT 2
- Questions about code HOT 1
- Question about loss HOT 5
- Question about paper [# model parameter] HOT 6
- Question about Within-Class Online Clustering HOT 8
- Question regarding IoUs of pretrained HRNet Proto HOT 3
- Question about seed HOT 3
- Layernorm in Prototype learning HOT 1
- Please how to continue training on the previous model, setting --resume_continue y does not take effect
- How long is the speed of each picture in the model test?
- intra-class prototypes is same. HOT 2
- Can't run the code because of dims
- awesome work!
- Question about the existence of the normalization, and how to run it
- Initial Prototype
- 'ce_weight' parameter in loss
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