We lifted 19 functions out of this paper's own repositories and ran 14 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/dice | canonical | 14 of 19 |
| Function | Status | Where it lives |
|---|---|---|
| conv1x1 | Ran | deeplearning-wisc/dice/models/resnetv2.py code served (permissive licence) · get_code("ee80cf467065076b") |
| conv3x3 | Ran | deeplearning-wisc/dice/models/resnet.py code served (permissive licence) · get_code("fac5364e2f53c6db") |
| conv3x3 | Ran | deeplearning-wisc/dice/models/resnetv2.py code served (permissive licence) · get_code("f01b8d3901f0b289") |
| create_dat | Ran | deeplearning-wisc/dice/util/lbtoolbox.py code served (permissive licence) · get_code("c89634a262bdea21") |
| dir | Ran | deeplearning-wisc/dice/util/common.py code served (permissive licence) · get_code("28ea57a8f1e61ffb") |
| format_time | Ran | deeplearning-wisc/dice/util/common.py code served (permissive licence) · get_code("8d7ec010c29e813b") |
| get_msp_score | Ran | deeplearning-wisc/dice/util/score.py code served (permissive licence) · get_code("8511dff685e73d9d") |
| get_rowl_score | Ran | deeplearning-wisc/dice/util/score.py code served (permissive licence) · get_code("3574d883f0468bc9") |
| get_sofl_score | Ran | deeplearning-wisc/dice/util/score.py code served (permissive licence) · get_code("f3bc884f0d0d3994") |
| log_f | Ran | deeplearning-wisc/dice/util/common.py code served (permissive licence) · get_code("b7607d4d2ee295e2") |
| pil_loader | Ran | deeplearning-wisc/dice/util/dataset_largescale.py code served (permissive licence) · get_code("bfe25354cd38a1d7") |
| resnet18 | Ran | deeplearning-wisc/dice/models/resnet.py code served (permissive licence) · get_code("8e06933dc2a722a3") |
| tf2th | Ran | deeplearning-wisc/dice/models/resnetv2.py code served (permissive licence) · get_code("7023eb86801bb3a3") |
| unpickle | Ran | deeplearning-wisc/dice/util/dataset_largescale.py code served (permissive licence) · get_code("80aea57f14a9774f") |
| get_mixup | Not yet run | deeplearning-wisc/dice/util/hyperrule.py code served (permissive licence) · get_code("7d63034d0bfadd78") |
| get_resolution | Not yet run | deeplearning-wisc/dice/util/hyperrule.py code served (permissive licence) · get_code("a933bb17fdb8ebb4") |
| get_resolution_from_dataset | Not yet run | deeplearning-wisc/dice/util/hyperrule.py code served (permissive licence) · get_code("53256b67d70b0daf") |
| load_dat | Not yet run | deeplearning-wisc/dice/util/lbtoolbox.py code served (permissive licence) · get_code("dab6ee6664758d5f") |
| resnet50 | Not yet run | deeplearning-wisc/dice/models/resnet.py code served (permissive licence) · get_code("8b37ce7c90ef9b25") |
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. Previous methods commonly rely on an OOD score derived from the overparameterized weight space, while largely overlooking the role of sparsification. In this paper, we reveal important insights that reliance on unimportant weights and units can directly attribute to the brittleness of OOD detection. To mitigate the issue, we propose a sparsification-based OOD detection framework termed DICE. Our key idea is to rank weights based on a measure of contribution, and selectively use the most salient weights to derive the output for OOD detection. We provide both empirical and theoretical insights, characterizing and explaining the mechanism by which DICE improves OOD detection. By pruning away noisy signals, DICE provably reduces the output variance for OOD data, resulting in a sharper output distribution and stronger separability from ID data. We demonstrate the effectiveness of sparsification-based OOD detection on several benchmarks and establish competitive performance.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2111.09805")
get_code_for_paper("2111.09805")
have("2111.09805")
Connect an agent — have() is free.