Zongbo Han, Changqing Zhang, Qinghua Hu, Yichen Bai, Bing Cao, Xiaoheng Jiang
We lifted 7 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 |
|---|---|---|
| ycfate/id-like | canonical | 3 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| basic_clean | Ran | ycfate/id-like/clip/simple_tokenizer.py pointer only (licence: NONE) · get_code("98f385d847636a3e") |
| get_pairs | Ran | ycfate/id-like/clip/simple_tokenizer.py pointer only (licence: NONE) · get_code("d919ae32e5e4e616") |
| whitespace_clean | Ran | ycfate/id-like/clip/simple_tokenizer.py pointer only (licence: NONE) · get_code("9542161e9640b858") |
| build_model | Not yet run | ycfate/id-like/clip/model.py pointer only (licence: NONE) · get_code("aa56b568a90f8516") |
| get_loss | Not yet run | ycfate/id-like/utils/id_like_loss.py pointer only (licence: NONE) · get_code("39f12edcbfe6882e") |
| load | Not yet run | ycfate/id-like/clip/clip.py pointer only (licence: NONE) · get_code("88d0e8acec4aead0") |
| select_in_out | Not yet run | ycfate/id-like/utils/id_like.py pointer only (licence: NONE) · get_code("6a21d81fd81655e0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples, especially discovering challenging outliers from auxiliary outliers dataset to improve OOD detection. However, they may still face limitations in effectively distinguishing between the most challenging OOD samples that are much like in-distribution (ID) data, i.e., ID-like samples. To this end, we propose a novel OOD detection framework that discovers ID-like outliers using CLIP [28] from the vicinity space of the ID samples, thus helping to identify these most challenging OOD samples. Then a prompt learning framework is proposed that utilizes the identified ID-like outliers to further leverage the capabilities of CLIP for OOD detection. Benefiting from the powerful CLIP, we only need a small number of ID samples to learn the prompts of the model without exposing other auxiliary outlier datasets. By focusing on the most challenging ID-like OOD samples and elegantly exploiting the capabilities of CLIP, our method achieves superior few-shot learning performance on various real-world image datasets (e.g., in 4-shot OOD detection on the ImageNet-1k dataset, our method reduces the average FPR95 by 12.16% and improves the average AUROC by 2.76%, compared to state-of-the-art methods).
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2311.15243")
get_code_for_paper("2311.15243")
have("2311.15243")
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