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..
WLD-Reg: A Data-Dependent Within-Layer Diversity Regularizer
Authors: Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present an extensive empirical study confirming that the proposed approach enhances the performance of several stateof-the-art neural network models in multiple tasks. |
| Researcher Affiliation | Academia | 1 Faculty of Information Technology and Communication Sciences, Tampere University, Finland 2 Faculty of Information Technology, University of Jyv askyl a, Finland 3 DIGIT, Department of Electrical and Computer Engineering, Aarhus University, Denmark |
| Pseudocode | Yes | Algorithm 1: One epoch of training with WLD-Reg |
| Open Source Code | Yes | The code is publically available at https://github.com/firasl/AAAI-23WLD-Reg. |
| Open Datasets | Yes | CIFAR10 and CIFAR100 (Krizhevsky, Hinton et al. 2009)., Image Net-2012 classification dataset (Russakovsky et al. 2015) |
| Dataset Splits | Yes | We split the original training set (50,000) into two sets: we use the first 40,000 images as the main training set and the last 10,000 as a validation set for hyperparameters optimization. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU, CPU models, or cloud computing instances) used for conducting the experiments. |
| Software Dependencies | No | The paper mentions optimizers like 'stochastic gradient descent (SGD)' but does not provide specific software dependencies or versions for libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | All the models are trained using stochastic gradient descent (SGD) with a momentum of 0.9, weight decay of 0.0001, and a batch size of 128 for 200 epochs. The initial learning rate is set to 0.1 and is then decreased by a factor of 5 after 60, 120, and 160 epochs, respectively. and For the hyperparameters, we fix λ1 = λ2 = 0.001 and γ = 10 for all the different approaches. |