We lifted 9 functions out of this paper's own repositories and ran 8 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 |
|---|---|---|
| hyunsoocho77/lacl-official | canonical | 8 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| Supervised_NT_xent | Ran | hyunsoocho77/lacl-official/loss.py code served (permissive licence) · get_code("8009738e5585203d") |
| collate_fn | Ran | hyunsoocho77/lacl-official/utils.py code served (permissive licence) · get_code("24b2927037c0889f") |
| collate_fn2 | Ran | hyunsoocho77/lacl-official/utils.py code served (permissive licence) · get_code("e5c1aadbdcf0a0eb") |
| fpr_at_95 | Ran | hyunsoocho77/lacl-official/evaluation.py code served (permissive licence) · get_code("1388ea7bf1dcf255") |
| get_sim_mat | Ran | hyunsoocho77/lacl-official/loss.py code served (permissive licence) · get_code("3aa2f062aac00de2") |
| mean_pooling | Ran | hyunsoocho77/lacl-official/model.py code served (permissive licence) · get_code("7e7ca6d67f5c378d") |
| merge_keys | Ran | hyunsoocho77/lacl-official/evaluation.py code served (permissive licence) · get_code("779bf38fd1a03f1f") |
| reg_loss | Ran | hyunsoocho77/lacl-official/loss.py code served (permissive licence) · get_code("bdbebc60e2bde280") |
| LACL_load_aug | Not yet run | hyunsoocho77/lacl-official/utils.py code served (permissive licence) · get_code("5cb7b677118e1dba") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience. Most recent studies in OOD detection utilize the information from a single representation that resides in the penultimate layer to determine whether the input is anomalous or not. Although such a method is straightforward, the potential of diverse information in the intermediate layers is overlooked. In this paper, we propose a novel framework based on contrastive learning that encourages intermediate features to learn layer-specialized representations and assembles them implicitly into a single representation to absorb rich information in the pre-trained language model. Extensive experiments in various intent classification and OOD datasets demonstrate that our approach is significantly more effective than other works.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2210.11034")
get_code_for_paper("2210.11034")
have("2210.11034")
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