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..
Constructing Orthogonal Convolutions in an Explicit Manner
Authors: Tan Yu, Jun Li, YUNFENG CAI, Ping Li
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on CIFAR-10 and CIFAR-100 demonstrate that the proposed ECO convolution is faster than SOC in evaluation while leading to competitive standard and certified robust accuracies. |
| Researcher Affiliation | Industry | Tan Yu, Jun Li, Yunfeng Cai, Ping Li Cognitive Computing Lab Baidu Research 10900 NE 8th St. Bellevue, Washington 98004, USA EMAIL |
| Pseudocode | Yes | Algorithm 1: The proposed efficient orthogonal convolution. ... Algorithm 2: Constructing the mapping matrix I |
| Open Source Code | No | The paper states, "It has been implemented in the Paddle Paddle deep learning platform https://www.paddlepaddle.org.cn." However, this refers to the platform used for implementation, not an explicit release of the authors' own source code for the method described in the paper. |
| Open Datasets | Yes | Experiments on CIFAR-10 and CIFAR-100 demonstrate that the proposed ECO convolution is faster than SOC in evaluation while leading to competitive standard and certified robust accuracies. |
| Dataset Splits | No | The paper mentions training and testing on CIFAR-10 and CIFAR-100 but does not explicitly provide details about a validation dataset split (e.g., percentages or counts) or its use. |
| Hardware Specification | Yes | The training is conducted on a single NVIDIA V100 GPU with 32G memory. |
| Software Dependencies | No | The paper mentions "Paddle Paddle deep learning platform" but does not specify a version number for this platform or any other software dependencies. |
| Experiment Setup | Yes | The training takes 200 epochs. The initial learning rate is 0.1 when the number of convolution layers is larger than 25 and 0.05 otherwise, and it decreases by a factor of 0.1 at the 50th and the 150th epoch. We set the weight decay as 5e-4. ... By default, we set T = 5 in training and T = 10 in testing. |