• Corpus ID: 235435759

Revisiting Model Stitching to Compare Neural Representations

@article{Bansal2021RevisitingMS,
  title={Revisiting Model Stitching to Compare Neural Representations},
  author={Yamini Bansal and Preetum Nakkiran and Boaz Barak},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.07682}
}
We revisit and extend model stitching (Lenc & Vedaldi 2015) as a methodology to study the internal representations of neural networks. Given two trained and frozen models A and B, we consider a “stitched model” formed by connecting the bottom-layers of A to the top-layers of B, with a simple trainable layer between them. We argue that model stitching is a powerful and perhaps under-appreciated tool, which reveals aspects of representations that measures such as centered kernel alignment (CKA… 

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Similarity and Matching of Neural Network Representations
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