Summary of Unveiling the Backbone-optimizer Coupling Bias in Visual Representation Learning, by Siyuan Li et al.
Unveiling the Backbone-Optimizer Coupling Bias in Visual Representation Learning
by Siyuan Li, Juanxi Tian, Zedong Wang, Luyuan Zhang, Zicheng Liu, Weiyang Jin, Yang Liu, Baigui Sun, Stan Z. Li
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the interplay between vision backbones and optimizers, uncovering an interconnected phenomenon called backbone-optimizer coupling bias (BOCB). It finds that certain CNN architectures, such as VGG and ResNet, exhibit a strong co-dependency with SGD-based optimizers, while more recent designs like ViTs and ConvNeXt are tightly coupled with adaptive learning rate optimizers. The study shows that BOCB can be introduced by both optimizers and specific backbone designs, significantly impacting pre-training and fine-tuning of vision models. Through empirical analysis, the paper provides insights into recommended optimizers and robust vision backbone architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how the part of a computer vision model (backbone) works with the way it’s trained (optimizer). The study finds that some common backbones like VGG and ResNet work well with certain types of optimizers, while newer designs like ViTs and ConvNeXt need different optimizers. It also shows that this connection can affect how well a model is trained and used for other tasks. The paper hopes to inspire others to think about these connections and create better computer vision systems. |
Keywords
» Artificial intelligence » Cnn » Fine tuning » Resnet
