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Pretrained models for ResNet in SO(2) and SE(3) #73
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Hi, there are existing methods which demonstrate that one does not need to retrain an equivariant version of ResNet (or other large pretrained models) to obtain pretrained equivariant ResNet, rather you can "adapt" a pretrained ResNet to be equivariant to a certain group with architecture-agnostic equivariance methods, such as canonicalization. Please feel free to check out: Equivariant Adaptation of Large Pretrained Model, NeurIPS 2023. Although it shows great results for discrete groups in the image domain and continuous groups in point clouds and other tasks, there are a few challenges in adapting pretrained image models for continuous groups, which is a work in progress. |
Hi Siba, |
Hi, thank you for taking a look at the paper! We are planning a release of our user-friendly library before the end of February. We are adding examples and tutorials for people to get started with canonicalization. I will let you know once we release the library. A schematic of the pipeline is described in Figure 2. Yes, the canonicalization networks are similar to the notebook you have linked. We give a small detail of hyperparameter tuning in Appendix Section B. We tune for different values of the number of layers, kernel sizes, dropout (switching off dropout generally helped), and learning rates. Anyways, the canonicalization networks are extensively small compared to the actual pretrained model under consideration, which makes it lucrative (some parameter sizes are highlighted in Table 3). |
I pretrained some equivariant ResNets on ImageNet-1k. The models and weights can be found here. The canonicalization approach is appealing since it can be applied to any pre-trained method. I haven't had a chance to compare against it yet, but I'm curious if there is any performance gap. |
Hello all,
I wonder if anyone has a pretrained model available for the equivariant ResNet from this example and the SE(3) equivariant model in this other example, using a bigger dataset like Imagenet or similar.
Thanks!
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