The balancing principle for parameter choice in distance-regularized domain adaptation
Authors: Werner Zellinger, Natalia Shepeleva, Marius-Constantin Dinu, Hamid Eghbal-zadeh, Hoan Duc Nguyen, Bernhard Nessler, Sergei Pereverzyev, Bernhard A. Moser
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically investigate the performance of our approach based on two target error bounds, two parameter selection methods, three datasets and different domain adaptation methods. |
| Researcher Affiliation | Collaboration | 1Software Competence Center Hagenberg Gmb H 2Institute for Machine Learning, Johannes Kepler University Linz 3Dynatrace Research 4Institute of Computational Perception, Johannes Kepler University Linz 5LIT AI Lab, Johannes Kepler University Linz 6Johann Radon Institute for Computational and Applied Mathematics, Austrian Academy of Sciences |
| Pseudocode | Yes | Algorithm 1: Balancing principle for domain adaptation (BPDA) |
| Open Source Code | Yes | The source-code can be found at https://github.com/Xpitfire/bpda |
| Open Datasets | Yes | We also use the Amazon Reviews dataset [53]. ... Our third dataset is the Domain Net 2019 dataset ... [56]. |
| Dataset Splits | No | We follow [23] and use held-out validation, i.e. we hold out a part of the training data as validation set, and we compute the importance weights based on this validation set. The paper states that a part of the training data is held out for validation but does not specify the exact proportion or number of samples for this validation split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Res Net-18' and general methods, but does not provide specific version numbers for programming languages, libraries, or frameworks (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | No | The details of all neural network architectures used, as well as the training strategy and hyperparameters are provided in the supplementary material. |