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..
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
Authors: Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that CSD either matches or beats state of the art approaches for domain generalization based on domain erasure, domain perturbed data augmentation, and meta-learning. Further diagnostics on rotated MNIST, where domains are interpretable, confirm the hypothesis that CSD successfully disentangles common and domain specific components and hence leads to better domain generalization; moreover, our code and dataset are publicly available at the following URL: https: //github.com/vihari/csd. Section 4. Experiments. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Indian Institute of Technology, Mumbai, India 2Microsoft Research, Bangalore, India. |
| Pseudocode | Yes | Algorithm 1 Common-Specific Low-Rank Decomposition (CSD ) and Algorithm 2 minwc,Ws,Γ f(wc, Ws, Γ). |
| Open Source Code | Yes | moreover, our code and dataset are publicly available at the following URL: https: //github.com/vihari/csd. |
| Open Datasets | Yes | The Lipit K dataset2 earlier used in (Shankar et al., 2018) is a Devanagari Character dataset... (2) Nepali Hand Written Character Dataset (Nepali C)3 contains data collected from 41 different people... PACS4 is a popular domain generalization benchmark... Speech utterances dataset We use the utterance data released by Google... Rotated MNIST and Fashion-MNIST: Rotated MNIST is a popular benchmark for evaluating domain generalization. |
| Dataset Splits | Yes | We train three different models on each of 25, 50, and 76 domains, and test on a disjoint set of 20 domains while using 10 domains for validation. Since the number of available domains is small, in this case we create a fixed split of 27 domains for training, 5 for validation and remaining 9 for testing. We use ten percent of total number of domains for each of validation and test. |
| Hardware Specification | No | The paper mentions neural network architectures used (Res Net-18, Le Net) but does not provide specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | Yes | The base classifier and the preprocessing pipeline for the utterances are borrowed from the implementation provided in the Tensorflow examples6. (footnote 6 points to tensorflow/tree/r1.15) |
| Experiment Setup | Yes | CSD is relatively stable to hyper-parameter choice, we set the default rank to 1, and parameters of weighted loss to λ = 1 and κ = 1. These hyper-parameters along with learning rates of all other methods as well as number of meta-train/meta-test domains for MASF and step size of perturbation in CG are all picked using a task-specific development set. |