Empowering Networks With Scale and Rotation Equivariance Using A Similarity Convolution
Authors: Zikai Sun, Thierry Blu
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the efficacy of our approach in the image classification task, demonstrating its robustness and the generalization ability to both scaled and rotated inputs.5 EXPERIMENTS |
| Researcher Affiliation | Academia | Zikai Sun and Thierry Blu Department of Electronic Engineering, The Chinese University of Hong Kong zksun@link.cuhk.edu.hk, thierry.blu@m4x.org |
| Pseudocode | Yes | Algorithm 1: Pipeline of our SREN Algorithm |
| Open Source Code | No | The paper does not contain any explicit statement about releasing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | The MNIST-ROT-12K DATASET (Larochelle et al., 2007) is commonly used to evaluate rotation-equivariant algorithms.To evaluate the generalization ability of our method, we conduct experiments on the STL-10 dataset Coates et al. (2011). |
| Dataset Splits | No | Specifically, we pad the images to 56 56 pixels, keep the training set unchanged, and randomly apply rotations, scalings, and translations to each test image within the ranges of θ = [0, 2π), s = [1, 2[, and t = 10. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using Adam optimizer but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We use Adam optimizer (Kingma & Ba, 2015) with a weight decay of 0.01, initialize the weights with Xavier (Glorot & Bengio, 2010), and set the learning rate to 0.01, which decays by a factor of 0.1 every 50 epochs. We set the batch size to 128 and stop training after 200 epochs.The network is trained for 1000 epochs with a batch size of 128, using Adam as the optimizer. The initial learning rate is set to 0.1 and adjusted using a cosine annealing schedule during training. |