Shuang Li, Binhui Xie, Longhui Yuan
We lifted 5 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| BIT-DA/RoTTA | — | 2 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| ColorJitterPro | Ran | BIT-DA/RoTTA/core/adapter/rotta.py code served (permissive licence) · get_code("a0c7ae238f5f94ce") |
| timeliness_reweighting | Ran | BIT-DA/RoTTA/core/adapter/rotta.py code served (permissive licence) · get_code("2fd0610ee153f304") |
| BaseAdapter | Not yet run | BIT-DA/RoTTA/core/adapter/rotta.py code served (permissive licence) · get_code("8d1f9b683822a8a0") |
| RoTTA | Not yet run | BIT-DA/RoTTA/core/adapter/rotta.py code served (permissive licence) · get_code("a381237713615fae") |
| get_tta_transforms | Not yet run | BIT-DA/RoTTA/core/adapter/rotta.py code served (permissive licence) · get_code("2810e57d868cd873") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Test-time adaptation (TTA) intends to adapt the pretrained model to test distributions with only unlabeled test data streams. Most of the previous TTA methods have achieved great success on simple test data streams such as independently sampled data from single or multiple distributions. However, these attempts may fail in dynamic scenarios of real-world applications like autonomous driving, where the environments gradually change and the test data is sampled correlatively over time. In this work, we explore such practical test data streams to deploy the model on the fly, namely practical test-time adaptation (PTTA). To do so, we elaborate a Robust Test-Time Adaptation (RoTTA) method against the complex data stream in PTTA. More specifically, we present a robust batch normalization scheme to estimate the normalization statistics. Meanwhile, a memory bank is utilized to sample category-balanced data with consideration of timeliness and uncertainty. Further, to stabilize the training procedure, we develop a time-aware reweighting strategy with a teacher-student model. Extensive experiments prove that RoTTA enables continual testtime adaptation on the correlatively sampled data streams. Our method is easy to implement, making it a good choice for rapid deployment. The code is publicly available at https://github.com/BIT-DA/RoTTA
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2303.13899")
get_code_for_paper("2303.13899")
have("2303.13899")
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