Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Information-Theoretic Domain Adaptation Under Severe Noise Conditions
Authors: Wei Wang, Hao Wang, Zhi-Yong Ran, Ran He
AAAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 18,933 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,972 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two real-world domain adaptation tasks demonstrate the superiority of our method. and In this section, we evaluate the proposed method in two domain adaptation related applications: 1) object recognition and 2) face recognition. |
| Researcher Affiliation | Collaboration | Wei Wang,1 Hao Wang,2 Zhi-Yong Ran,3 Ran He4 1Institute of Software, Chinese Academy of Sciences, Beijing 100190, China. 2360 Search Lab, Qihoo 360, Beijing 100190, China. 3Chongqing University of Posts and Telecommunications, Chongqing 400065, China. 4Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. |
| Pseudocode | Yes | Algorithm 1 Robust Information-Theoretic Domain Adaptation (RIDA) |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for the described methodology, nor does it include a direct link to a code repository. |
| Open Datasets | Yes | COIL-20 (Nene et al. 1996) dataset contains 1,440 images from 20 objects. and CMU-PIE (Sim, Baker, and Bsat 2002) is a benchmark face dataset which includes 41,368 face images from 68 individuals with different poses, illuminations and facial expressions. |
| Dataset Splits | No | The paper describes partitioning datasets into source and target domains (e.g., COIL1 and COIL2 for COIL-20) and constructing cross-domain datasets, but it does not specify explicit train/validation/test splits, percentages, or sample counts needed for reproduction. It refers to 'labeled source data' and 'unlabeled target data' but not clear dataset split ratios. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running the experiments. It only discusses the general experimental setup without hardware specifications. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, that would be needed to replicate the experiment. |
| Experiment Setup | Yes | Our method involves three parameters: λ1, λ2 and σ. Across the experiments, we set these parameters by searching the values in the range [10 3, 103]. In general, our method is found to be robust to these parameters. |