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..
Let Go of Your Labels with Unsupervised Transfer
Authors: Artyom Gadetsky, Yulun Jiang, Maria Brbic
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate TURTLE on a diverse benchmark suite of 26 datasets and show that it achieves new state-of-the-art unsupervised performance. |
| Researcher Affiliation | Academia | 1EPFL, Lausanne, Switzerland. |
| Pseudocode | Yes | The pseudocode of TURTLE is provided in Algorithm B1 with implementation details in Appendix B.3. |
| Open Source Code | Yes | Code is publicly available at https://github.com/mlbio-epfl/turtle. |
| Open Datasets | Yes | We study the performance of TURTLE on the extensive benchmark of 26 vision datasets (Radford et al., 2021). The detailed description of each dataset is provided in Appendix B.1. |
| Dataset Splits | Yes | We use 10-fold cross-validation to select the best task. |
| Hardware Specification | Yes | The selection process takes a few minutes on small datasets, and around 8 hours on Image Net, with a single NVIDIA A100 GPU. |
| Software Dependencies | No | We use the cu ML.Logistic Regression (Raschka et al., 2020) for linear probe evaluation in our paper 3. The cu ML library allows for GPU acceleration... ADAM (Kingma & Ba, 2015) optimizer is used for the training of both linear classifier and task encoder. |
| Experiment Setup | Yes | We use 10000 as the default batch-size. We update the linear classifier for M = 10 steps at each iteration and train the task encoder for T = 6000 iterations in total. If not specifically mentioned, we set the entropy regularization parameter γ = 10 for all experiments. We do a grid search over 5 different learning rates for both task encoder and linear classifier with η {0.01, 0.005, 0.001, 0.0005, 0.0001}, respectively. |