We lifted 4 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| xihongyang1999/dealmvc | canonical | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| cluster_acc | Ran | xihongyang1999/dealmvc/metric.py pointer only (licence: NONE) · get_code("6a157a961548e0ac") |
| evaluate | Ran | xihongyang1999/dealmvc/metric.py pointer only (licence: NONE) · get_code("141e6f3460ee3670") |
| purity | Ran | xihongyang1999/dealmvc/metric.py pointer only (licence: NONE) · get_code("36822efe025e6afb") |
| load_data | Not yet run | xihongyang1999/dealmvc/dataloader.py pointer only (licence: NONE) · get_code("24e576183cd7cca3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Benefiting from the strong view-consistent information mining capacity, multi-view contrastive clustering has attracted plenty of attention in recent years. However, we observe the following drawback, which limits the clustering performance from further improvement. The existing multi-view models mainly focus on the consistency of the same samples in different views while ignoring the circumstance of similar but different samples in cross-view scenarios. To solve this problem, we propose a novel Dual contrastive calibration network for Multi-View Clustering (DealMVC). Specifically, we first design a fusion mechanism to obtain a global cross-view feature. Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph. Moreover, to utilize the diversity of multi-view information, we propose a local contrastive calibration loss to constrain the consistency of pair-wise view features. The feature structure is regularized by reliable class information, thus guaranteeing similar samples have similar features in different views. During the training procedure, the interacted cross-view feature is jointly optimized at both local and global levels. In comparison with other state-of-the-art approaches, the comprehensive experimental results obtained from eight benchmark datasets provide substantial validation of the effectiveness and superiority of our algorithm. We release the code of DealMVC at https://github.com/xihongyang1999/DealMVC on GitHub.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2308.09000")
get_code_for_paper("2308.09000")
have("2308.09000")
Connect an agent — have() is free.