Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
Authors: David M. Knigge, David W Romero, Erik J Bekkers
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach across several vision datasets, and show that our weight sharing leads to improved performance and computational efficiency. In many settings, separable G-CNNs outperform their nonseparable counterpart, while only using a fraction of their training time. In addition, thanks to the increase in computational efficiency, we are able to implement G-CNNs equivariant to the Sim(2) group; the group of dilations, rotations and translations of the plane. Sim(2)-equivariance further improves performance on all tasks considered, and achieves state-of-the-art performance on rotated MNIST. |
| Researcher Affiliation | Academia | 1University of Amsterdam, The Netherlands 2Vrije Universiteit Amsterdam, The Netherlands. Correspondence to: David M. Knigge <d.m.knigge@uva.nl>. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. It provides mathematical derivations and conceptual diagrams. |
| Open Source Code | Yes | Code is available on Github. |
| Open Datasets | Yes | Rotated MNIST The 62.000 MNIST images (Le Cun et al., 1998) are split into a training, validation and test set of 10.000, 2.000 and 50.000 images respectively, and randomly rotated to orientations between [0, 2π). ... CIFAR10 We evaluate our models on the CIFAR10 dataset, containing 62.000 32 32 color images in 10 balanced classes (Krizhevsky et al., 2009). |
| Dataset Splits | Yes | Rotated MNIST The 62.000 MNIST images (Le Cun et al., 1998) are split into a training, validation and test set of 10.000, 2.000 and 50.000 images respectively, and randomly rotated to orientations between [0, 2π). |
| Hardware Specification | Yes | All models are trained on a single Titan V. |
| Software Dependencies | No | The paper mentions 'Pytorch conv2d' but does not specify version numbers for PyTorch or any other software dependencies, which are necessary for full reproducibility. |
| Experiment Setup | Yes | All architectures are trained with Adam optimisation (Kingma & Ba, 2014), and 1e 4 weight decay. All models trained on rotated MNIST, except for the state-of-the-art runs detailed in B.2, are trained for 200 epochs with a batch size of 128 and a learning rate of 1 10 4. ... For the SIREN, we used an architecture of two hidden layers of 64 units. We found a value for ω0 of 10 to work well in all our experiments. |