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..
DeNetDM: Debiasing by Network Depth Modulation
Authors: Silpa Vadakkeeveetil Sreelatha, Adarsh Kappiyath, ABHRA CHAUDHURI, Anjan Dutta
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive experiments and ablation studies on a diverse set of datasets, including synthetic datasets like Colored MNIST and Corrupted CIFAR-10, as well as real-world datasets, Biased FFHQ, BAR and Celeb A, demonstrating an approximate 5% improvement over existing methods. |
| Researcher Affiliation | Collaboration | 1 University of Surrey 2 University of Exeter 3 Fujitsu Research of Europe |
| Pseudocode | Yes | The pseudocode for the entire training process of De Net DM is provided in Section 7.4. |
| Open Source Code | Yes | The project page is available at https://vssilpa.github.io/denetdm/. ... Source code is provided in https://github.com/kadarsh22/De Net DM. |
| Open Datasets | Yes | Datasets: We evaluate the performance of De Net DM across diverse domains using two synthetic datasets (Colored MNIST Ahuja et al. (2020), Corrupted CIFAR10 Hendrycks and Dietterich (2019)) and three real-world datasets (Biased FFHQ Kim et al. (2021), BAR Nam et al. (2020)) and Celeb A Liu et al. (2015). |
| Dataset Splits | Yes | We perform extensive hyperparameter tuning using a small unbiased validation set with bias annotations to obtain the deep and shallow branches for all the datasets. |
| Hardware Specification | Yes | Experimental compute: We utilize RTX 3090 GPUs for all our experiments. |
| Software Dependencies | No | The paper mentions Py Torch but does not specify a version number. It also mentions CUDA but without a version. |
| Experiment Setup | Yes | Table 12: Optimal hyperparameters for the CMNIST, C-CIFAR10, BAR and BFFHQ datasets determined through extensive experimentation. The tuples represent optimal hyperparameters for Stage 1 and Stage 2, respectively. Parameter: Learning Rate (LR), Batch Size, Momentum, Weight Decay, Epochs. |