Comments (1)
qian-qi
commented on August 16, 2024
Hi,
- dim和C和你说的一致.
- 实验中参数表现还是比较stable的，可能在不同数据集上需要调一下K，保证K小于每个class里样本的数量.
- SoftTriple本身就是个分类loss，所以我们没试过和分类的联合训练.
- 模型的backbone是在ImageNet上pre-train过的，loss里的params是learn from scratch的，所以lr不一样.
- 我们在实验中没发现过不理想的情况. 因为在worst case下，i.e., 类内没有多个clusters的结构，SoftTriple也可以degenerate到标准的normalized SoftMax loss.
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Related Issues (16)
- proof of proposition1 HOT 1
- centers = F.normalize(self.fc, p=2, dim=0) what is its purpose？ HOT 1
- proposition1 HOT 1
- The BatchNorm layers are not completely frozen HOT 1
- train loss HOT 3
- can you explain the recall@k? HOT 2
- pretrained models HOT 2
- out of memory error HOT 4
- Code and Paper don't seem to match... HOT 2
- Out of Memory error when training on big number of classes HOT 2
- Clarification on gamma and lambda HOT 2
- MemoryError: Unable to allocate 113. GiB for an array with shape (122994, 122994) and data type float64 HOT 1
- loss is nan HOT 7
- Question - data with unit length HOT 1
- Centers
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