We lifted 10 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| virajprabhu/clue | canonical | 5 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| kmeans_plus_plus_opt | Ran | virajprabhu/clue/utils.py code served (permissive licence) · get_code("887ef76a0f0b84bd") |
| outer_product_opt | Ran | virajprabhu/clue/utils.py code served (permissive licence) · get_code("e331f59f46d29459") |
| process_txt | Ran | virajprabhu/clue/preprocess_domainnet.py code served (permissive licence) · get_code("6962ebebbe6f9056") |
| register_model | Ran | virajprabhu/clue/adapt/models/models.py code served (permissive licence) · get_code("8fadf1baab2bb38b") |
| register_solver | Ran | virajprabhu/clue/adapt/solvers/solver.py code served (permissive licence) · get_code("b6020de74b4ef8f6") |
| get_model | Not yet run | virajprabhu/clue/adapt/models/models.py code served (permissive licence) · get_code("2efe87863253f8ba") |
| get_solver | Not yet run | virajprabhu/clue/adapt/solvers/solver.py code served (permissive licence) · get_code("6a049409bc8f279e") |
| get_strategy | Not yet run | virajprabhu/clue/sample.py code served (permissive licence) · get_code("dbcd7c2c6017db5f") |
| register_strategy | Not yet run | virajprabhu/clue/sample.py code served (permissive licence) · get_code("37dd93cc2f3a2f57") |
| row_norms | Not yet run | virajprabhu/clue/utils.py code served (permissive licence) · get_code("dfb55f13a69a3302") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Generalizing deep neural networks to new target domains is critical to their real-world utility. In practice, it may be feasible to get some target data labeled, but to be cost-effective it is desirable to select a maximally-informative subset via active learning (AL). We study the problem of AL under a domain shift, called Active Domain Adaptation (Active DA). We demonstrate how existing AL approaches based solely on model uncertainty or diversity sampling are less effective for Active DA. We propose Clustering Uncertainty-weighted Embeddings (CLUE), a novel label acquisition strategy for Active DA that performs uncertainty-weighted clustering to identify target instances for labeling that are both uncertain under the model and diverse in feature space. CLUE consistently outperforms competing label acquisition strategies for Active DA and AL across learning settings on 6 diverse domain shifts for image classification.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2010.08666")
get_code_for_paper("2010.08666")
have("2010.08666")
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