We lifted 18 functions out of this paper's own repositories and ran 15 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 |
|---|---|---|
| scottclowe/zs-ssl-clustering | canonical | 15 of 18 |
| Function | Status | Where it lives |
|---|---|---|
| build_affinity_matrix | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/louvain.py code served (permissive licence) · get_code("f08f66e56561b459") |
| determine_epoch_seed | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/utils.py code served (permissive licence) · get_code("da25bbcd7de8221b") |
| generate_id | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/utils.py code served (permissive licence) · get_code("1bf2e9396bc00670") |
| get_continuous_class_map | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/datasets/nabirds.py code served (permissive licence) · get_code("d7aa9c6a98fa4fc5") |
| get_embeddings_path | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/io.py code served (permissive licence) · get_code("f3d48f6c34f39530") |
| get_pred_path | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/io.py code served (permissive licence) · get_code("f96c64779258568e") |
| get_randsizecrop_transform | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/data_transformations.py code served (permissive licence) · get_code("4ebdf4346a9ed2e7") |
| get_transform | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/data_transformations.py code served (permissive licence) · get_code("c0cd4e224662e2f7") |
| image_dataset_sizes | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/datasets/api.py code served (permissive licence) · get_code("2252d616ba4317bf") |
| load_class_names | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/datasets/nabirds.py code served (permissive licence) · get_code("d53209ecea508922") |
| load_hierarchy | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/datasets/nabirds.py code served (permissive licence) · get_code("02112df93daa57bf") |
| probe_embedding_shape | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/probe.py code served (permissive licence) · get_code("c9646fb0b16f0e8b") |
| sanitize_filename | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/io.py code served (permissive licence) · get_code("3a3579dedf3fab37") |
| scale_lr | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/probe.py code served (permissive licence) · get_code("3b2cc66fa66500b3") |
| setup_linear_classifiers | Ran | scottclowe/zs-ssl-clustering/zs_ssl_clustering/probe.py code served (permissive licence) · get_code("506400c268aaa688") |
| fetch_dataset | Not yet run | scottclowe/zs-ssl-clustering/zs_ssl_clustering/datasets/api.py code served (permissive licence) · get_code("d0fc1c7854a8ad46") |
| fetch_image_dataset | Not yet run | scottclowe/zs-ssl-clustering/zs_ssl_clustering/datasets/api.py code served (permissive licence) · get_code("94ded4fcf407632d") |
| init_or_resume_wandb_run | Not yet run | scottclowe/zs-ssl-clustering/zs_ssl_clustering/utils.py code served (permissive licence) · get_code("c5b56f5bbb6ef6cd") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Can pretrained models generalize to new datasets without any retraining? We deploy pretrained image models on datasets they were not trained for, and investigate whether their embeddings form meaningful clusters. Our suite of benchmarking experiments use encoders pretrained solely on ImageNet-1k with either supervised or self-supervised training techniques, deployed on image datasets that were not seen during training, and clustered with conventional clustering algorithms. This evaluation provides new insights into the embeddings of self-supervised models, which prioritize different features to supervised models. Supervised encoders typically offer more utility than SSL encoders within the training domain, and vice-versa far outside of it, however, fine-tuned encoders demonstrate the opposite trend. Clustering provides a way to evaluate the utility of self-supervised learned representations orthogonal to existing methods such as kNN. Additionally, we find the silhouette score when measured in a UMAP-reduced space is highly correlated with clustering performance, and can therefore be used as a proxy for clustering performance on data with no ground truth labels. Our code implementation is available at \url{https://github.com/scottclowe/zs-ssl-clustering/}.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.02465")
get_code_for_paper("2406.02465")
have("2406.02465")
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