Yixuan Li, Rui Huang
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 |
|---|---|---|
| deeplearning-wisc/large_scale_ood | — | 1 of 1 |
| deeplearning-wisc/gradnorm_ood | — | 1 of 1 |
| tmlr-group/class_prior | — | 1 of 1 |
| ma-kjh/CMA-OoDD | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| calc_group_softmax_loss | Ran | deeplearning-wisc/large_scale_ood/finetune.py code served (permissive licence) · get_code("1caff2f8b8fc899d") |
| get_Mahalanobis_score | Ran | deeplearning-wisc/gradnorm_ood/utils/mahalanobis_lib.py code served (permissive licence) · get_code("ae64ecb508b5b2bb") |
| iterate_data_mahalanobis | Ran | tmlr-group/class_prior/funcs.py code served (permissive licence) · get_code("12fc2270fe050752") |
| CLIP | Not yet run | ma-kjh/CMA-OoDD/clip/model.py pointer only (licence: NONE) · get_code("ef2d645ece6b584c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. Existing solutions are mainly driven by small datasets, with low resolution and very few class labels (e.g., CIFAR). As a result, OOD detection for largescale image classification tasks remains largely unexplored. In this paper, we bridge this critical gap by proposing a group-based OOD detection framework, along with a novel OOD scoring function termed MOS. Our key idea is to decompose the large semantic space into smaller groups with similar concepts, which allows simplifying the decision boundaries between in-vs. out-of-distribution data for effective OOD detection. Our method scales substantially better for high-dimensional class space than previous approaches. We evaluate models trained on ImageNet against four carefully curated OOD datasets, spanning diverse semantics. MOS establishes state-of-the-art performance, reducing the average FPR95 by 14.33% while achieving 6x speedup in inference compared to the previous best method.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2105.01879")
get_code_for_paper("2105.01879")
have("2105.01879")
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