Comments (1)
Hi @yyang2xin , thanks for the question.
(1) For the ideal scenario, if the features are separable, then there exist several algorithms that can guarantee a perfect solution. As you suggested, both methods work well under that situation. We can consider another extreme situation that each data sample belongs to a different cluster, then if we apply a high threshold for all methods to partition each sample to a cluster, then we can obtain the same perfect results.
(2) If the feature extractor is not perfect, the performance gap depends on the data. Stronger features are likely to produce both better baseline and better learning-based clustering models. As the power of feature extractor increases, the performance gap may first enlarge and then decrease.
(3) As stated in the paper, we use standard ResNet-50 and softmax in our experiments. CDP gives an analysis on stronger backbone, which shows that stronger backbone brings further performance gain.
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Related Issues (20)
- output error HOT 1
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