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..
Gradual Domain Adaptation without Indexed Intermediate Domains
Authors: Hong-You Chen, Wei-Lun Chao
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validated IDOL on two data sets studied in [35], including Rotated MNIST [36] and Portraits over years [14]. IDOL can successfully discover the domain sequence that leads to comparable GDA performance to using the pre-defined sequence (i.e., by side information). |
| Researcher Affiliation | Academia | Hong-You Chen The Ohio State University, USA EMAIL Wei-Lun Chao The Ohio State University, USA EMAIL |
| Pseudocode | Yes | Meta-reweighting for Equation 8 can be implemented via the following six steps for multiple iterations. 1. Detach: θ θm, 2. Forward: θ(q) θ ηθ |U\m| P i U\m qi ℓ(f(xi; θ), sharpen(f(xi; θm))) 3. Detach: θ θ(q), 4. Backward: θ(q) θ(q) ηθ |Um| P j Um ℓ(f(xj; θ(q)), sharpen(f(xj; θ ))) 5. Update: q q ηq |Um| P j Um ℓ(f(xj; θ(q)), sharpen(f(xj; θm))) 6. Update: qi max{0, qi}. |
| Open Source Code | Yes | Codes are available at https://github.com/hongyouc/IDOL. |
| Open Datasets | Yes | We validated IDOL on two data sets studied in [35], including Rotated MNIST [36] and Portraits over years [14]. |
| Dataset Splits | Yes | For both datasets, each domain contains 2000 images, and 1, 000 images are reserved for both the source and target domains for validation. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or types of compute clusters) used for running the experiments are mentioned, beyond the general acknowledgment of computational resources by the Ohio Supercomputer Center. |
| Software Dependencies | No | The paper mentions 'Adam optimizer [32]' but does not provide specific version numbers for software dependencies such as programming languages or libraries. |
| Experiment Setup | Yes | We follow the setup in [35]: each model is a convolutional neural network trained for 20 epochs for each domain consequently (including training on the source data), using Adam optimizer [32] with a learning rate 0.001, batch size 32, and weight decay 0.02. We use this optimizer as the default if not specified. Hyper-parameters of IDOL include K = 2M rounds for progressive training and 30 epochs of refinement per step (with mini-batch 128), where M = 19 for the Rotated MNIST and M = 7 for the Portraits. |