Micah Goldblum, Marc Finzi, Andrew Wilson, Nate Gruver, Vit Mixer
We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ngruver/lie-deriv | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| get_layerwise | Ran | ngruver/lie-deriv/exps_layerwise.py code served (permissive licence) · get_code("8a33d76693af815e") |
| numparams | Ran | ngruver/lie-deriv/exps_e2e.py code served (permissive licence) · get_code("689af1734307b300") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Equivariance guarantees that a model's predictions capture key symmetries in data. When an image is translated or rotated, an equivariant model's representation of that image will translate or rotate accordingly. The success of convolutional neural networks has historically been tied to translation equivariance directly encoded in their architecture. The rising success of vision transformers, which have no explicit architectural bias towards equivariance, challenges this narrative and suggests that augmentations and training data might also play a significant role in their performance. In order to better understand the role of equivariance in recent vision models, we apply the Lie derivative, a method for measuring equivariance with strong mathematical foundations and minimal hyperparameters. Using the Lie derivative, we study the equivariance properties of hundreds of pretrained models, spanning CNNs, transformers, and Mixer architectures. The scale of our analysis allows us to separate the impact of architecture from other factors like model size or training method. Surprisingly, we find that many violations of equivariance can be linked to spatial aliasing in ubiquitous network layers, such as pointwise non-linearities, and that as models get larger and more accurate they tend to display more equivariance, regardless of architecture. For example, transformers can be more equivariant than convolutional neural networks after training.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2210.02984")
get_code_for_paper("2210.02984")
have("2210.02984")
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