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..
Discrepancy-Based Active Learning for Domain Adaptation
Authors: Antoine de Mathelin, François Deheeger, Mathilde MOUGEOT, Nicolas Vayatis
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments show that the proposed algorithm is competitive against other state-of-the-art active learning techniques in the context of domain adaptation, in particular on large data sets of around one hundred thousand images. |
| Researcher Affiliation | Collaboration | Antoine de Mathelin1,2, Franc ois Deheeger1, Mathilde Mougeot3,2, Nicolas Vayatis2 1Michelin, 2Centre Borelli, Universit e Paris-Saclay, CNRS, ENS Paris-Saclay, 3ENSIIE |
| Pseudocode | Yes | Algorithm 1 Accelerated K-medoids; Algorithm 2 K-Medoids Greedy; Algorithm 3 Branch & Bound Medoid (B & B) |
| Open Source Code | Yes | The source code is provided on Git Hub 1. https://github.com/antoinedemathelin/dbal |
| Open Datasets | Yes | We choose Superconductivity (Hamidieh, 2018; Dua & Graff, 2017); The office data set (Saenko et al., 2010); a synthetic digits data set: SYNTH is used to learn a classification task for a data set of real digits pictures: SVHN (Street-View House Number) (Netzer et al., 2011). |
| Dataset Splits | No | The paper states 'fine-tuning of the optimization hyper-parameters (epochs, batch sizes...) is performed using only source labeled data.' This implies a validation process but does not specify how the data itself was split into distinct training, validation, and test sets with specific percentages or counts. |
| Hardware Specification | Yes | The experiments have been run on a (2.7GHz, 16G RAM) computer. |
| Software Dependencies | No | The paper mentions 'Python 3.8', but it does not specify version numbers for other key libraries or tools like PyTorch, scikit-learn (which is cited but no version is given for its use in this paper), ADAPT2, or Adam optimizer. |
| Experiment Setup | Yes | We use a learning rate of 0.001, a number of epochs of 100, a batch size of 128 and the mean squared error as loss function. |