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..
Domain Adaptation with Adversarial Training on Penultimate Activations
Authors: Tao Sun, Cheng Lu, Haibin Ling
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on popular UDA benchmarks under both standard setting and source-data free setting. The results validate that our method achieves the best scores against previous arts. |
| Researcher Affiliation | Collaboration | Tao Sun 1, Cheng Lu 2, Haibin Ling 1 1 Stony Brook University, USA 2 XPeng Motors, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https: //github.com/tsun/APA. |
| Open Datasets | Yes | Datasets. Office-Home (OH) has 65 classes from four domains: Artistic (A), Clip Art (C), Product (P), and Realworld (R). We use both the original version and the RS-UT (Reverse-unbalanced Source and Unbalanced Target) version (Tan, Peng, and Saenko 2020) that is manually created to have a large label shift. Vis DA-2017 (Peng et al. 2017) is a synthetic-to-real dataset of 12 objects. Domain Net (Peng et al. 2019) (DN) is a large UDA benchmark. We use the 40-class version (Tan, Peng, and Saenko 2020) from four domains: Clipart (C), Painting (P), Real (R), Sketch (S). |
| Dataset Splits | No | The paper describes the use of source and target domains and discusses training parameters, but it does not explicitly state specific train/validation/test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper states 'We implement our methods with Py Torch.' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For all tasks, we use batch size 16, β = 0.1, τ = 0.75, ϵ = 30 for APAu, ϵ = 1.0 for APAn, with an only exception on Vis DA where we use β = 0.04 for APAn instead. |