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 [1].
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. |