A Layer Selection Approach to Test Time Adaptation
Authors: Sabyasachi Sahoo, Mostafa ElAraby, Jonas Ngnawe, Yann Batiste Pequignot, Frédéric Precioso, Christian Gagné
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we demonstrate that the proposed layer selection framework improves the performance of existing TTA approaches across multiple datasets, domain shifts, model architectures, and TTA losses. |
| Researcher Affiliation | Academia | 1IID, Universit e Laval 2Mila 3Universit e de Montr eal 4Universit e Cote d Azur, CNRS, INRIA, I3S, Maasai 5Canada CIFAR AI Chair EMAIL |
| Pseudocode | No | The paper describes the proposed approach using mathematical equations and descriptive text, but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block with structured steps. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing code, nor does it provide links to any code repositories or mention code in supplementary materials. |
| Open Datasets | Yes | This section compares our proposed approaches with existing baselines on Domainbed (Gulrajani and Lopez-Paz 2021), a popular benchmark with large single distribution shifts, and Continual TTA, a popular benchmark with multiple distribution shifts. TTA losses Two popular TTA losses are considered: Pseudo-Labeling (PL) (Lee et al. 2013) and SHOT (Liang, Hu, and Feng 2020). |
| Dataset Splits | Yes | For the experiments on Domainbed, we follow the evaluation protocol as described in Iwasawa and Matsuo (2021), including dataset splits for the following four datasets: PACS (Li et al. 2017), VLCS (Fang, Xu, and Rockmore 2013), Terra Incognita (Beery, Van Horn, and Perona 2018), and Office-Home (Venkateswara et al. 2017). |
| Hardware Specification | No | Computations were made on the cedar, and beluga supercomputers, managed by Calcul Qu ebec and the Digital Research Alliance of Canada (Alliance). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | Implementational details We report results for GALA with window size of 20 and selection threshold of 0.75 with single-layer granularity. It appears that GALA is not overly sensitive to hyperparameters, and those values work well overall see Sec. 5 for more discussion on hyperparameter values and the design choices. |