Unveiling the Backbone-Optimizer Coupling Bias in Visual Representation Learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914968133173248 |
|---|---|
| author | Li, Siyuan Tian, Juanxi Wang, Zedong Zhang, Luyuan Liu, Zicheng Jin, Weiyang Liu, Yang Sun, Baigui Li, Stan Z. |
| author_facet | Li, Siyuan Tian, Juanxi Wang, Zedong Zhang, Luyuan Liu, Zicheng Jin, Weiyang Liu, Yang Sun, Baigui Li, Stan Z. |
| contents | This paper delves into the interplay between vision backbones and optimizers, unvealing an inter-dependent phenomenon termed \textit{\textbf{b}ackbone-\textbf{o}ptimizer \textbf{c}oupling \textbf{b}ias} (BOCB). We observe that canonical CNNs, such as VGG and ResNet, exhibit a marked co-dependency with SGD families, while recent architectures like ViTs and ConvNeXt share a tight coupling with the adaptive learning rate ones. We further show that BOCB can be introduced by both optimizers and certain backbone designs and may significantly impact the pre-training and downstream fine-tuning of vision models. Through in-depth empirical analysis, we summarize takeaways on recommended optimizers and insights into robust vision backbone architectures. We hope this work can inspire the community to question long-held assumptions on backbones and optimizers, stimulate further explorations, and thereby contribute to more robust vision systems. The source code and models are publicly available at https://bocb-ai.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06373 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unveiling the Backbone-Optimizer Coupling Bias in Visual Representation Learning Li, Siyuan Tian, Juanxi Wang, Zedong Zhang, Luyuan Liu, Zicheng Jin, Weiyang Liu, Yang Sun, Baigui Li, Stan Z. Computer Vision and Pattern Recognition Machine Learning This paper delves into the interplay between vision backbones and optimizers, unvealing an inter-dependent phenomenon termed \textit{\textbf{b}ackbone-\textbf{o}ptimizer \textbf{c}oupling \textbf{b}ias} (BOCB). We observe that canonical CNNs, such as VGG and ResNet, exhibit a marked co-dependency with SGD families, while recent architectures like ViTs and ConvNeXt share a tight coupling with the adaptive learning rate ones. We further show that BOCB can be introduced by both optimizers and certain backbone designs and may significantly impact the pre-training and downstream fine-tuning of vision models. Through in-depth empirical analysis, we summarize takeaways on recommended optimizers and insights into robust vision backbone architectures. We hope this work can inspire the community to question long-held assumptions on backbones and optimizers, stimulate further explorations, and thereby contribute to more robust vision systems. The source code and models are publicly available at https://bocb-ai.github.io/. |
| title | Unveiling the Backbone-Optimizer Coupling Bias in Visual Representation Learning |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2410.06373 |