Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Truly Scale-Equivariant Deep Nets with Fourier Layers
Authors: Md Ashiqur Rahman, Raymond A. Yeh
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct our experiments on the MNIST-scale [40] and STL [4] dataset. By design, our method achieves zeros scale equivariance-error both in theory and in practice. In terms of accuracy, we compare to recent scale-equivariant CNNs. We found our approach to be competitive in classification accuracy and exhibit better data efficiency in low-resource settings. Our contributions are as follows: We conduct extensive experiments validating the proposed approach. On MNIST and STL datasets, the proposed model achieves an absolute zero end-to-end scale-equivariance error while maintaining competitive classification accuracy. |
| Researcher Affiliation | Academia | Md Ashiqur Rahman Raymond A. Yeh Department of Computer Science, Purdue University EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access (link, explicit statement of release) to open-source code for the methodology. |
| Open Datasets | Yes | We conduct our experiments on the MNIST-scale [40] and STL [4] dataset. Each image in the original MNIST dataset is randomly downsampled with a factor of [ 1 0.3 1], such that every resolution from 8 8 to 28 28 contains an equal number of samples. Each image of the dataset is randomly scaled with a randomly chosen downsampling factor between [1 2] such that every resolution from 48 to 97 contains an equal number of samples. |
| Dataset Splits | Yes | We used 10k, 2k, and 50k for training, validation, and test set samples. We use 7k, 1k, and 5k samples in our training, validation, and test set. |
| Hardware Specification | No | The paper mentions general hardware terms like "Modern GPUs" and "executed on a GPU" but does not specify any exact models or detailed specifications of the hardware used for experiments. |
| Software Dependencies | No | The paper mentions using "Adam optimizer" but does not provide specific version numbers for any software libraries or dependencies (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For the baselines and CNN, we follow the implementation, hyperparameters, and architecture provided in prior works [41, 42]. For Fourier CNN, we use the Fourier block introduced in the Fourier Neural operator [18]. Inspired by their design, we use 1 1 complex convolution in the Fourier domain along with the scale-equivariant convolution. We follow the baseline for all training hyper-parameters, except we included a weight decay of 0.01. All of the models are trained for 250 epochs with Adam optimizer with an initial learning rate of 0.01. The learning rate is reduced by a factor of 0.1 after every 100 epoch. |