We lifted 2 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| visual-ai/dissect-ood-osr | canonical | 0 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| get_model | Not yet run | visual-ai/dissect-ood-osr/train/arpl_conv.py pointer only (licence: NONE) · get_code("3ac113774af8ab00") |
| get_optimizer | Not yet run | visual-ai/dissect-ood-osr/train/arpl_conv.py pointer only (licence: NONE) · get_code("7245c363b773354c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Detecting test-time distribution shift has emerged as a key capability for safely deployed machine learning models, with the question being tackled under various guises in recent years. In this paper, we aim to provide a consolidated view of the two largest sub-fields within the community: out-of-distribution (OOD) detection and open-set recognition (OSR). In particular, we aim to provide rigorous empirical analysis of different methods across settings and provide actionable takeaways for practitioners and researchers. Concretely, we make the following contributions: (i) We perform rigorous cross-evaluation between state-of-the-art methods in the OOD detection and OSR settings and identify a strong correlation between the performances of methods for them; (ii) We propose a new, large-scale benchmark setting which we suggest better disentangles the problem tackled by OOD detection and OSR, re-evaluating state-of-the-art OOD detection and OSR methods in this setting; (iii) We surprisingly find that the best performing method on standard benchmarks (Outlier Exposure) struggles when tested at scale, while scoring rules which are sensitive to the deep feature magnitude consistently show promise; and (iv) We conduct empirical analysis to explain these phenomena and highlight directions for future research. Code: https://github.com/Visual-AI/Dissect-OOD-OSR
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2408.16757")
get_code_for_paper("2408.16757")
have("2408.16757")
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