We lifted 6 functions out of this paper's own repositories and ran 6 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 |
|---|---|---|
| cleanlab/ood-detection-benchmarks | canonical | 6 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| find_dupes_in_data_dir | Ran | cleanlab/ood-detection-benchmarks/src/preprocess/remove_dupes.py code served (permissive licence) · get_code("137e31a502097297") |
| find_dupes_in_data_split_dir | Ran | cleanlab/ood-detection-benchmarks/src/preprocess/remove_dupes.py code served (permissive licence) · get_code("a8307d3e9159b810") |
| fit_mahalanobis | Ran | cleanlab/ood-detection-benchmarks/src/experiments/OOD/mahalanobis.py code served (permissive licence) · get_code("4d49690d4932ba8c") |
| fit_rmd | Ran | cleanlab/ood-detection-benchmarks/src/experiments/OOD/mahalanobis.py code served (permissive licence) · get_code("cc65480057d34f19") |
| score_mahalanobis | Ran | cleanlab/ood-detection-benchmarks/src/experiments/OOD/mahalanobis.py code served (permissive licence) · get_code("1e5ce5936af7b546") |
| timefunc | Ran | cleanlab/ood-detection-benchmarks/src/utils/time_utils.py code served (permissive licence) · get_code("f88aae44b2b0fdac") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We study simple methods for out-of-distribution (OOD) image detection that are compatible with any already trained classifier, relying on only its predictions or learned representations. Evaluating the OOD detection performance of various methods when utilized with ResNet-50 and Swin Transformer models, we find methods that solely consider the model's predictions can be easily outperformed by also considering the learned representations. Based on our analysis, we advocate for a dead-simple approach that has been neglected in other studies: simply flag as OOD images whose average distance to their K nearest neighbors is large (in the representation space of an image classifier trained on the in-distribution data).
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2207.03061")
get_code_for_paper("2207.03061")
have("2207.03061")
Connect an agent — have() is free.