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..
A Closer Look at Smoothness in Domain Adversarial Training
Authors: Harsh Rangwani, Sumukh K Aithal, Mayank Mishra, Arihant Jain, Venkatesh Babu Radhakrishnan
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively verify the empirical efficacy of SDAT over DAT across various datasets for classification (i.e., Domain Net, Vis DA-2017 and Office-Home) with Res Net and Vision Transformer (Dosovitskiy et al., 2020) (Vi T) backbones. We also show a prototypical application of SDAT in DA for object detection, demonstrating it s diverse applicability. |
| Researcher Affiliation | Collaboration | 1Video Analytics Lab, Indian Institute of Science, Bengaluru, India 2PES University, Bengaluru 3Amazon, India (Work done at Indian Institute of Science, Bengaluru). |
| Pseudocode | Yes | L. Py Torch Pseudocode for SDAT |
| Open Source Code | Yes | The source code used for experiments is available at: https://github.com/val-iisc/SDAT. |
| Open Datasets | Yes | We evaluate our proposed method on three datasets: Office Home, Vis DA-2017, and Domain Net... Office-Home (Venkateswara et al., 2017):... Domain Net (Peng et al., 2019):... Vis DA-2017 (Peng et al., 2017): |
| Dataset Splits | Yes | We split the target data into train and validation sets and report the best m AP on validation data. |
| Hardware Specification | Yes | All the above experiments were run on Nvidia V100, RTX 2080 and RTX A5000 GPUs. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2019)', 'Detectron2 (Wu et al., 2019)', and 'Wandb (Biewald, 2020)' with citations indicating their year of publication/release, but it does not provide explicit version numbers (e.g., PyTorch 1.9) for these software components. |
| Experiment Setup | Yes | We use a learning rate of 0.01 with batch size 32 in all of our experiments with Res Net backbone... The ρ value is set to 0.02 for the Office-Home experiments, 0.005 for the Vis DA-2017 experiments and 0.05 for the Domain Net experiments. ...We train it for a total of 30 epochs with 1000 iterations per epoch. The momentum parameter in SGD is set to 0.9 and a weight decay of 0.001 is used. |