Ming Lin, Xiuyu Sun, Yaohua Wang, Yaobin Zhang, Fangyi Zhang, Yuqi Zhang, Senzhang Wang
We lifted 15 functions out of this paper's own repositories and ran 11 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 |
|---|---|---|
| damo-cv/Ada-NETS | canonical | 11 of 14 |
| Thomas-wyh/Ada-NETS | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| batch_search | Ran | damo-cv/Ada-NETS/tool/faiss_search.py code served (permissive licence) · get_code("bb6e98a818ab83bb") |
| build_symmetric_adj | Ran | damo-cv/Ada-NETS/AND/adjacency.py code served (permissive licence) · get_code("b47e4da5605e550d") |
| clusters2labels | Ran | damo-cv/Ada-NETS/GCN/cluster.py code served (permissive licence) · get_code("9cb5361facb80118") |
| filter_knns | Ran | damo-cv/Ada-NETS/tool/knn.py code served (permissive licence) · get_code("960270dfb8856837") |
| gcn_v | Ran | damo-cv/Ada-NETS/AND/net/gcn_v.py code served (permissive licence) · get_code("f3041fc06ca7bbed") |
| get_neg_loss | Ran | damo-cv/Ada-NETS/GCN/net/optim_modules.py code served (permissive licence) · get_code("72c104b3d72d0dd9") |
| get_pos_loss | Ran | damo-cv/Ada-NETS/GCN/net/optim_modules.py code served (permissive licence) · get_code("9ee109079dbd413e") |
| knns2ordered_nbrs | Ran | damo-cv/Ada-NETS/tool/knn.py code served (permissive licence) · get_code("227696c1d71b8892") |
| row_normalize | Ran | damo-cv/Ada-NETS/AND/adjacency.py code served (permissive licence) · get_code("ce0784d592bd0d41") |
| sparse_mx_to_indices_values | Ran | damo-cv/Ada-NETS/AND/adjacency.py code served (permissive licence) · get_code("f73847ac9f20f56a") |
| sqeuclidean_pdist | Ran | damo-cv/Ada-NETS/GCN/net/optim_modules.py code served (permissive licence) · get_code("6bd747fe350f9027") |
| format | Not yet run | damo-cv/Ada-NETS/GCN/cluster.py code served (permissive licence) · get_code("b49214d884be3833") |
| get_Rstarset | Not yet run | Thomas-wyh/Ada-NETS/tool/struct_space.py code served (permissive licence) · get_code("874120f76adf0d08") |
| get_topK | Not yet run | damo-cv/Ada-NETS/tool/max_Q_ind.py code served (permissive licence) · get_code("020579fb712da25b") |
| knns_recall | Not yet run | damo-cv/Ada-NETS/tool/knn.py code served (permissive licence) · get_code("6979c33355f8cd0a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Face clustering has attracted rising research interest recently to take advantage of massive amounts of face images on the web. State-of-the-art performance has been achieved by Graph Convolutional Networks (GCN) due to their powerful representation capacity. However, existing GCN-based methods build face graphs mainly according to kNN relations in the feature space, which may lead to a lot of noise edges connecting two faces of different classes. The face features will be polluted when messages pass along these noise edges, thus degrading the performance of GCNs. In this paper, a novel algorithm named Ada-NETS is proposed to cluster faces by constructing clean graphs for GCNs. In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images. Then, an adaptive neighbour discovery strategy is proposed to determine a proper number of edges connecting to each face image. It significantly reduces the noise edges while maintaining the good ones to build a graph with clean yet rich edges for GCNs to cluster faces. Experiments on multiple public clustering datasets show that Ada-NETS significantly outperforms current state-of-the-art methods, proving its superiority and generalization. Code is available at https://github.com/damo-cv/Ada-NETS.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2202.03800")
get_code_for_paper("2202.03800")
have("2202.03800")
Connect an agent — have() is free.