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..
Deep Asymmetric Transfer Network for Unbalanced Domain Adaptation
Authors: Daixin Wang, Peng Cui, Wenwu Zhu
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on two real-world datasets demonstrate that DATN attains a substantial gain over state-of-the-art methods. |
| Researcher Affiliation | Academia | Daixin Wang, Peng Cui, Wenwu Zhu Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | The full algorithm is shown in Alg. 1. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of open-source code for the described methodology. |
| Open Datasets | Yes | In our experiments, we use two real-world datasets, i.e. NUS-WIDE and AMAZON REVIEWS. NUS-WIDE (Chua et al. 2009) is a public web image dataset... AMAZON REVIEWS (Prettenhofer and Stein 2010) is a cross-language dataset... |
| Dataset Splits | Yes | The final values of all the parameters are determined by using 5-fold cross-validation on the training set. |
| Hardware Specification | No | The paper states 'Our approach is implemented in Tensorflow' but does not provide any specific details about the hardware (e.g., GPU, CPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Our approach is implemented in Tensorflow' but does not specify a version number for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | The values of α and β are selected from {0, 0.5, 1, 2, 5, 10}. The regularization parameters of λ and λ are set as 0.1 and 0.0001. Throughout the experiments, the learning rate is set as 0.0001, the decay is set as 0.8 and the momentum is set as 0.8. Table 3: Number of neurons of each layer of DATN. |