Evaluation of Test-Time Adaptation Under Computational Time Constraints
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2023
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916256006799360 |
|---|---|
| author | Alfarra, Motasem Itani, Hani Pardo, Alejandro Alhuwaider, Shyma Ramazanova, Merey Pérez, Juan C. Cai, Zhipeng Müller, Matthias Ghanem, Bernard |
| author_facet | Alfarra, Motasem Itani, Hani Pardo, Alejandro Alhuwaider, Shyma Ramazanova, Merey Pérez, Juan C. Cai, Zhipeng Müller, Matthias Ghanem, Bernard |
| contents | This paper proposes a novel online evaluation protocol for Test Time Adaptation (TTA) methods, which penalizes slower methods by providing them with fewer samples for adaptation. TTA methods leverage unlabeled data at test time to adapt to distribution shifts. Although many effective methods have been proposed, their impressive performance usually comes at the cost of significantly increased computation budgets. Current evaluation protocols overlook the effect of this extra computation cost, affecting their real-world applicability. To address this issue, we propose a more realistic evaluation protocol for TTA methods, where data is received in an online fashion from a constant-speed data stream, thereby accounting for the method's adaptation speed. We apply our proposed protocol to benchmark several TTA methods on multiple datasets and scenarios. Extensive experiments show that, when accounting for inference speed, simple and fast approaches can outperform more sophisticated but slower methods. For example, SHOT from 2020, outperforms the state-of-the-art method SAR from 2023 in this setting. Our results reveal the importance of developing practical TTA methods that are both accurate and efficient. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_04795 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Evaluation of Test-Time Adaptation Under Computational Time Constraints Alfarra, Motasem Itani, Hani Pardo, Alejandro Alhuwaider, Shyma Ramazanova, Merey Pérez, Juan C. Cai, Zhipeng Müller, Matthias Ghanem, Bernard Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition This paper proposes a novel online evaluation protocol for Test Time Adaptation (TTA) methods, which penalizes slower methods by providing them with fewer samples for adaptation. TTA methods leverage unlabeled data at test time to adapt to distribution shifts. Although many effective methods have been proposed, their impressive performance usually comes at the cost of significantly increased computation budgets. Current evaluation protocols overlook the effect of this extra computation cost, affecting their real-world applicability. To address this issue, we propose a more realistic evaluation protocol for TTA methods, where data is received in an online fashion from a constant-speed data stream, thereby accounting for the method's adaptation speed. We apply our proposed protocol to benchmark several TTA methods on multiple datasets and scenarios. Extensive experiments show that, when accounting for inference speed, simple and fast approaches can outperform more sophisticated but slower methods. For example, SHOT from 2020, outperforms the state-of-the-art method SAR from 2023 in this setting. Our results reveal the importance of developing practical TTA methods that are both accurate and efficient. |
| title | Evaluation of Test-Time Adaptation Under Computational Time Constraints |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2304.04795 |