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..
Category-Aware Active Domain Adaptation
Authors: Wenxiao Xiao, Jiuxiang Gu, Hongfu Liu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments and in-depth explorations demonstrate the efficacy of our method on category-aware active DA over three datasets. |
| Researcher Affiliation | Collaboration | 1Computer Science Department, Brandeis University, Waltham, Massachusetts, USA 2Adobe Research, USA. Correspondence to: Wenxiao Xiao <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Category-Aware Active DA for Category c |
| Open Source Code | Yes | Our code is available at https://github.com/wxxiaoss/Category Aware DA. |
| Open Datasets | Yes | We choose three popular DA benchmark datasets, Office-Home (Venkateswara et al., 2017), Domain Net126 (Peng et al., 2019) and Vis Da-2017 (Peng et al., 2017) in our experiments. |
| Dataset Splits | Yes | For the validation loss Lv = ℓ(V ; ˆθ), V being the validation set, the change after removing a training sample can further be estimated using ˆθLv H 1 ˆθ ˆθℓ(xi, yi), as demonstrated in the work by Koh & Liang (2017). ... we use all queried target samples from previous iterations, i.e., V = Lt, to calculate the validation loss Lv in Eq. (2). |
| Hardware Specification | Yes | We implement our model using Py Torch(Paszke et al., 2019) and scikit-learn (Buitinck et al., 2013) with one NVIDIA TITAN RTX GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch(Paszke et al., 2019) and scikit-learn (Buitinck et al., 2013)' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For all three datasets, we train the model for 5 iterations. For Office-Home (Venkateswara et al., 2017), we select 1% of the target data in each iteration. For the large datasets Vis Da-2017 (Peng et al., 2017) and Domain Net-126 (Peng et al., 2019), we select a fixed number of 100 samples in each iteration. ... We use the Res Net-50 (He et al., 2016) pre-trained on Image Net as the backbone feature extractor and train a DANN model as the base DA method following CLUE (Prabhu et al., 2021). ... In the following iterations, we actively query and annotate b target samples with Algorithm 1. |