Robust Domain Generalisation by Enforcing Distribution Invariance
Authors: Sarah M. Erfani, Mahsa Baktashmotlagh, Masud Moshtaghi, Vinh Nguyen, Christopher Leckie, James Bailey, Kotagiri Ramamohanarao
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Empirical Analysis In this section, we illustrate the effectiveness of ESRand via a visualisation of a toy dataset. Furthermore, we compare the performance and efficiency of the proposed algorithm with state-of-the-art algorithms through classification tasks on multiple benchmark datasets. |
| Researcher Affiliation | Academia | Department of Computing and Information Systems, The University of Melbourne, Australia. {sarah.erfani, masud.moshtaghi, vinh.nguyen, caleckie, baileyj, kotagiri}@unimelb.edu.au Department of Science and Engineering, Queensland University of Technology, Australia. m.baktashmotlagh@qut.edu.au |
| Pseudocode | No | The paper describes the ESRand procedure in narrative text in Section 3.3 but does not provide a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code or links to a code repository for the described methodology. |
| Open Datasets | Yes | The experiments are conducted on four real life datasets from the UCI Machine Learning Repository: (i) Daily and Sport Activity (DSA), (ii) Heterogeneity Activity Recognition (HAR), (iii) Opportunity Activity Recognition (OAR), (iv) PAMAP2 Physical Activity Monitoring... |
| Dataset Splits | Yes | The hyper-parameters of all the algorithms are adjusted using grid search based on their best performance on a validation set. The reported AUC values of each algorithm are the average accuracies of leave-one-domain-out test (domain), i.e., taking one domain as the test set and the remaining domains as the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "For SVM based methods LIBSVM was used" but does not specify a version number for LIBSVM or any other software dependencies. |
| Experiment Setup | Yes | k NN: k Nearest Neighbour, we use k = 1 |