Unveiling the Backbone-Optimizer Coupling Bias in Visual Representation
  Learning
This paper delves into the interplay between vision backbones and optimizers,
unvealing an inter-dependent phenomenon termed
\textbf{backbone-optimizer coupling bias}
(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/.
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Siyuan Li
claimed_verified
2024-10-13T20:18:06.834Z
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Siyuan Li
user
Lupin1998
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Juanxi Tian
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2024-10-11T07:44:18.996Z
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Juanxi Tian
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Juanxi
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Zedong Wang
claimed_verified
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Zedong Wang
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JackyWangAI
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Luyuan Zhang
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Zicheng Liu
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2024-10-10T09:26:46.409Z
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Zicheng Liu
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Weiyang Jin
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Weiyang Jin
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Wayne-King
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Yang Liu
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Baigui Sun
admin_assigned
2024-10-10T09:27:02.990Z
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Baigui Sun
user
sunbaigui
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Stan Z. Li
admin_assigned
2024-10-10T09:27:08.710Z
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Stan Z. Li
user
szli-0000
Unveiling the Backbone-Optimizer Coupling Bias in Visual Representation
  Learning
This paper delves into the interplay between vision backbones and optimizers,
unvealing an inter-dependent phenomenon termed
\textbf{backbone-optimizer coupling bias}
(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/.
The interplay between vision backbones and optimizers, termed Backbone-Optimizer Coupling Bias, significantly impacts pre-training and fine-tuning of vision models, with different architectures aligning with specific optimizer types.
backbone-optimizer coupling bias VGG ResNet SGD ViTs ConvNeXt adaptive learning rate pre-training downstream fine-tuning