Discrepancy-Based Active Learning for Domain Adaptation
Authors: Antoine de Mathelin, François Deheeger, Mathilde MOUGEOT, Nicolas Vayatis
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | 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. |