VirDA: Reusing Backbone for Unsupervised Domain Adaptation with Visual Reprogramming
Authors: Duc-Duy Nguyen, Dat Nguyen
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Vir DA on Office-31 and obtain 92.8% mean accuracy with only 1.5M trainable parameters. We conduct experiments to evaluate Vir DA s capability in classification effectiveness, training parameter size, and storage requirement for each source and target domain pair. Our experiments demonstrate that the proposed Vir DA, requiring only a maximum of 1.5 million of training parameters (less than 2% of PMTrans (Zhu et al., 2023)) and only 6 MB for storage per domain (compared to over 340 MB of both PMTrans and CDTrans (Xu et al., 2022)), and fully reusing the backbone s parameters, achieves comparable performance to state-of-the-art (SOTA) methods across standard domain adaptation benchmarks, including Office-31 (Saenko et al., 2010), Office-Home (Venkateswara et al., 2017), and Digits (MNIST (Le Cun et al., 1998), USPS (Netzer et al., 2011), SVHN (Hull, 1994)). |
| Researcher Affiliation | Academia | Duy Nguyen Hanoi University of Science and Technology EMAIL Dat Nguyen Harvard University EMAIL Basis.ai EMAIL |
| Pseudocode | No | The paper describes the methodology using mathematical equations and textual descriptions (e.g., Section 3, Equations 1-14), but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release our implementation and reproduction package at https://github.com/Duy-Nguyen-Duc/Vir DA |
| Open Datasets | Yes | We evaluate our proposed method on widely used three domain adaptation benchmarks, namely Office-31, Office-Home, and Digits... Digits is a dataset composed from three other digit datasets, which are MNIST (Le Cun et al., 1998), USPS (Hull, 1994), and Street View House Numbers (SVHN) (Hull, 1994). Office-31 (Saenko et al., 2010) is the most popular dataset for real-world domain adaptation. Office-Home (Venkateswara et al., 2017) is a more challenging benchmark than Office-31. |
| Dataset Splits | Yes | For the UDA test, we adopted three commonly used cross-dataset transfer settings with the standard data split: S M, U M, M U. We evaluated all methods on six domain adaptation tasks. |
| Hardware Specification | Yes | The experiment results reported here were obtained on a machine equipped with Intel Xeon Gold 6130 CPU at 2.10GHz clock speed with 16 cores and 64 GB of RAM running Linux and using a single NVIDIA GTX 3090 device. |
| Software Dependencies | No | The paper mentions using Adam W optimizer with specific hyperparameters but does not provide specific version numbers for software dependencies like programming languages, deep learning frameworks, or libraries. |
| Experiment Setup | Yes | In all experiments, we use both Resnet (He et al., 2016) and Vi T (Dosovitskiy et al., 2020) models pre-trained on Image Net (Deng et al., 2009) as the fixed backbone for Vir DA. For the Digits tasks, we use Res Net-18 with a learning rate of 3e 4 for the classifier heads and 5e 4 for the visual reprogramming modules, using a batch size of 128. The dropout rate for the classifier and the mask generator is set as pmask = 0.5 and p C = 0.3, respectively. On the Office-Home and Office-31 datasets, we adopt Vi T-B/32 as the backbone for all transfer tasks. We set the same learning rate as above, using a batch size of 32 and pmask = 0.3 with p C = 0.1. For all experiments, we adopt Adam W (Loshchilov & Hutter, 2017) with the default configuration of (β1, β2) is (0.9, 0.999), and a weight decay of 1e 5. On the Office-31 and Office-Home datasets, we set Lvr and Nvr corresponding to 6 and 5, for coarse object-level mask, while on Digits we set Lvr = 5 and Nvr = 4, as the dataset s characteristics demonstrate mild transformation. On all tasks, we set the number of forward passes to estimate uncertainty M = 4. We train our method in two phases, namely burn-in in 20 epochs and domain adaptation in 30 epochs for all data settings following Li et al. (2022). The scaling factor for each loss is set as αunc = 0.3, αadv = 0.1 and αintra = 0.15, in which these hyperparameters are obtained via grid-search. |