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..
Energy-Based Test Sample Adaptation for Domain Generalization
Authors: Zehao Xiao, Xiantong Zhen, Shengcai Liao, Cees G. M. Snoek
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on six benchmarks for classification of images and microblog threads demonstrate the effectiveness of our proposal. |
| Researcher Affiliation | Collaboration | 1AIM Lab, University of Amsterdam 2Inception Institute of Artificial Intelligence |
| Pseudocode | Yes | We provide the detailed training and test algorithm of our energy-based sample adaptation in Algorithm 1. |
| Open Source Code | Yes | 1 Code available: https://github.com/zzzx1224/EBTSA-ICLR2023. |
| Open Datasets | Yes | We conduct our experiments on five widely used datasets for domain generalization, PACS (Li et al., 2017), Office-Home (Venkateswara et al., 2017), Domain Net (Peng et al., 2019), and Rotated MNIST and Fashion-MNIST. ... PHEME (Zubiaga et al., 2016) |
| Dataset Splits | Yes | We use the same training and validation split as (Li et al., 2017) and follow their leaveone-out protocol. |
| Hardware Specification | Yes | We train all models on an NVIDIA Tesla V100 GPU for 10,000 iterations. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimization' and 'Distil BERT' but does not provide specific version numbers for any libraries or frameworks, which is required for reproducibility. |
| Experiment Setup | Yes | We use Adam optimization and train for 10,000 iterations with a batch size of 128. We set the learning rate to 0.00005 for Res Net-18, 0.00001 for Res Net-50, and 0.0001 for the energy-based model and classification model. We use 20 steps of Langevin dynamics sampling to adapt the target samples to source distributions, with a step size of 50. |