Equivariance with Learned Canonicalization Functions
Authors: Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our main hypothesis, supported by our empirical results, is that learning a small neural network to perform canonicalization is better than using predefined heuristics. Our experiments show that learning the canonicalization function is competitive with existing techniques for learning equivariant functions across many tasks, including image classification, N-body dynamics prediction, point cloud classification and part segmentation, while being faster across the board. |
| Researcher Affiliation | Collaboration | 1School of Computer Science, Mc Gill University, Montr eal, Canada 2Mila Quebec Artficial Intelligence Institute, Montr eal, Canada 3Samsung SAIT AI Lab, Montr eal, Canada 4DIRO, Universit e de Montr eal, Montr eal, Canada. |
| Pseudocode | Yes | I Algorithm for Image Inputs Algorithm 1 Differentiable Canonicalization for Image Inputs |
| Open Source Code | Yes | Our code is available at: https://github.com/ oumarkaba/canonical_network. |
| Open Datasets | Yes | We selected the Rotated MNIST dataset (Larochelle et al., 2007), often used as a benchmark for equivariant architectures. [...] We use the Model Net40 (Wu et al., 2015) and Shape Net (Chang et al., 2015) datasets for experiments on point clouds. |
| Dataset Splits | Yes | We perform early stopping based on the classification performance of the validation dataset with a patience of 20 epochs. |
| Hardware Specification | Yes | Inference time (in seconds) of the networks for Model Net40 classification test split in 1 A100 and 8 CPUs with a batch size of 32. |
| Software Dependencies | No | The paper mentions software like Adam, batch-norm, ReLU, Kornia, and a PYTORCH code snippet, but does not provide specific version numbers for any of these software components or libraries. |
| Experiment Setup | Yes | In all our image experiments, we train the models by minimizing the cross entropy loss for 100 epochs using Adam (Kingma & Ba, 2014) with a learning rate of 0.001. We perform early stopping based on the classification performance of the validation dataset with a patience of 20 epochs. |