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..
Deep invariant networks with differentiable augmentation layers
Authors: Cédric ROMMEL, Thomas Moreau, Alexandre Gramfort
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide empirical evidence showing that our approach is easier and faster to train than modern automatic data augmentation techniques based on bilevel optimization, while achieving comparable results. |
| Researcher Affiliation | Academia | Cédric Rommel, Thomas Moreau & Alexandre Gramfort Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France {firstname.lastname}@inria.fr |
| Pseudocode | No | The paper describes the architecture and method but does not include a formal pseudocode or algorithm block. |
| Open Source Code | Yes | The accompanying code can be found at https://github.com/cedricrommel/augnet. |
| Open Datasets | Yes | In this experiment we showcase Aug Net on a standard image recognition task using the CIFAR10 dataset [27]. |
| Dataset Splits | Yes | All models considered in this experiment are trained for 300 epochs over 5 different seeds on a random 80% fraction of the official CIFAR10 training set. The remaining 20% is used as a validation set for early-stopping and choosing hyperparameters, and the official test set is used for reporting performance metrics. |
| Hardware Specification | Yes | It was also granted access to the HPC resources of IDRIS under the allocation 2021-AD011012284R1 and 2022-AD011011172R2 made by GENCI. |
| Software Dependencies | No | Our work is based on code from [19] and [12], as well as open-source libraries like MNE-PYTHON [35] and BRAINDECODE [36]. |
| Experiment Setup | Yes | All models considered in this experiment are trained for 300 epochs over 5 different seeds on a random 80% fraction of the official CIFAR10 training set. The reader is referred to Section A.3 for further experimental details, and to Section B.2 for a sensitivity analysis on hyperparameters C and λ. |