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..
Self-training For Few-shot Transfer Across Extreme Task Differences
Authors: Cheng Perng Phoo, Bharath Hariharan
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 EXPERIMENTS, 5.1 FEW-SHOT TRANSFER ACROSS DRASTICALLY DIFFERENT DOMAINS, 5.1.1 RESULTS |
| Researcher Affiliation | Academia | Cheng Perng Phoo, Bharath Hariharan Department of Computer Science Cornell University EMAIL |
| Pseudocode | No | The paper describes its method using prose and equations, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/cpphoo/STARTUP. |
| Open Datasets | Yes | We experiment with the challenging (BSCD-FSL) benchmark introduced in Guo et al. (2020). The base dataset in this benchmark is mini Image Net (Vinyals et al., 2016)... Crop Diseases, Euro SAT, ISIC2018, Chest X |
| Dataset Splits | Yes | To pick the suitable starting learning rate, 10% of the unlabeled data and 5% of the labeled data (1% when using Image Net as the base dataset) are set aside as our internal validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or cloud computing specifications used for running experiments. |
| Software Dependencies | No | The paper mentions PyTorch and scikit-learn with citations, but does not explicitly state specific version numbers for these software dependencies (e.g., "PyTorch 1.9"). |
| Experiment Setup | Yes | The student model is trained for 1000 epochs... We use a batch size of 256... We use the SGD with momentum optimizer with momentum 0.9 and weight decay 1e-4. To pick the suitable starting learning rate, 10% of the unlabeled data and 5% of the labeled data... are set aside as our internal validation set. We pick the starting learning rate by training the student with starting learning rate lr {1e-1, 5e-2, 3e-2, 1e-2, 5e-3, 3e-3, 1e-3}... |