Test-time Adaptation in Non-stationary Environments via Adaptive Representation Alignment
Authors: Zhen-Yu Zhang, Zhiyu Xie, Huaxiu Yao, Masashi Sugiyama
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both benchmark datasets and a real-world application validate the effectiveness and adaptability of our proposed algorithm. Through benchmark experiments and a real-world application, we demonstrate the effectiveness and efficiency of our method in improving performance under non-stationary environments. |
| Researcher Affiliation | Academia | Zhen-Yu Zhang Center for Advanced Intelligence Project, RIKEN zhen-yu.zhang@riken.jp Zhiyu Xie Stanford University zhiyuxie@stanford.edu Huaxiu Yao UNC-Chapel Hill huaxiu@cs.unc.edu Masashi Sugiyama Center for Advanced Intelligence Project, RIKEN Graduate School of Frontier Sciences, The University of Tokyo sugi@k.u-tokyo.ac.jp |
| Pseudocode | Yes | Algorithm 1 Adaptive Representation Alignment 1: Initialization: i [K], ϕi t = ϕ0 2: for t = 1 to T do 3: for i = 0 to K do 4: if 2i mod t == 0 then 5: set ϕi t = ϕ0, Ri t = 0, Ci t = 0 6: end if 7: end for 8: update base learners by Eqn. (4) and update weight pt K according to Eqn. (6) 9: combine base learners according to Eqn. (5) 10: update classifier module according to Eqn. (7) 11: end for |
| Open Source Code | Yes | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: All the details of the data can be found in A.1. Codes can be found in Supplementary Material. |
| Open Datasets | Yes | We evaluate the proposed Ada-Re Align algorithm on two large-scale benchmark datasets: CIFAR10C and Image Net C. We further evaluate the proposed algorithm on a real-world wildlife species classification task using the i Wild Cam dataset [3] |
| Dataset Splits | No | The paper mentions training on 'clean data' and testing on 'corrupted data streams' but does not specify details for a validation split (percentages, counts, or how it was used) nor does it cite a standard split that includes validation. |
| Hardware Specification | Yes | We use the following compute resource configuration: 2 Xeon Gold 6242R with a base frequency of 3.1 GHz, 8 Ge Force 3090 with 24GB VRAM, and a total of 768GB RAM. |
| Software Dependencies | No | The operating system employed is Ubuntu 20.04. The paper does not provide specific version numbers for programming languages (e.g., Python) or libraries (e.g., PyTorch, scikit-learn) that would be needed for replication. |
| Experiment Setup | Yes | For our proposed algorithm, we use SGD as the update rule, with a momentum of 0.9 and a learning rate of 0.0005. The optimizer employed is SGD, with a momentum of 0.9 and a batch size of 32 for rounds where the number of data data exceeds 32. ... The learning rate is set to 0.0005. |