DeNetDM: Debiasing by Network Depth Modulation
Authors: Silpa Vadakkeeveetil Sreelatha, Adarsh Kappiyath, ABHRA CHAUDHURI, Anjan Dutta
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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. |