Hi, thanks for your great work! I have a question about the inference result. In the paper, it is mentioned that the inference results are obtained by conduct k-means clustering on the post-backbone features. From my understanding, we can have multiple place to obtain the inference result: (1) clustering results from the post-backbone features, as used in the paper; (2) clustering results from the post-projector features in the contrastive-learning branch; (3) prediction results directly from the classifier in the pseudo-labeling branch, used in SimGCD paper.
Is there any justification why you choose the first (1) choice? Have tried the option (2) and (3)? Which one do you think will be better? We plan to include your paper as our baseline, you insights on these questions will be greatly appreciated 💌
Hi @JianhongBai , when have have time, could you help provide some insights for the question above? We plan to include your paper as one of our baselines and your answers will greatly help us. Thanks a lot!
Hi, thanks for your great work! I have a question about the inference result. In the paper, it is mentioned that the inference results are obtained by conduct k-means clustering on the post-backbone features. From my understanding, we can have multiple place to obtain the inference result: (1) clustering results from the post-backbone features, as used in the paper; (2) clustering results from the post-projector features in the contrastive-learning branch; (3) prediction results directly from the classifier in the pseudo-labeling branch, used in SimGCD paper.
Is there any justification why you choose the first (1) choice? Have tried the option (2) and (3)? Which one do you think will be better? We plan to include your paper as our baseline, you insights on these questions will be greatly appreciated 💌