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..
Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning
Authors: Mayee Chen, Daniel Y Fu, Avanika Narayan, Michael Zhang, Zhao Song, Kayvon Fatahalian, Christopher Re
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate THANOS on two tasks designed to evaluate how well it preserves subclasses: Coarse-to-fine transfer learning trains a model to classify superclasses but use the representations to distinguish subclasses. THANOS outperforms Sup Con by 11.1 points on average across 5 standard datasets. Worst-group robustness evaluates how well a model can identify underperforming sub-groups and maintain high performance on them. THANOS identifies underperforming sub-groups 7.7 points better than previous work (Sohoni et al., 2020) and achieves 4.7 points of lift on worst-group robustness across 3 datasets, setting state-of-the-art on Celeb A by 11.5 points. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Stanford University 2Adobe Research. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/HazyResearch/thanos-code/. |
| Open Datasets | Yes | We use coarse versions of CIFAR10, CIFAR100, MNIST, and Tiny Image Net to study coarse-to-fine transfer. We use Waterbirds, ISIC and Celeb A for robustness (Sagawa et al., 2019; Codella et al., 2019; Liu et al., 2015; Sohoni et al., 2020). |
| Dataset Splits | No | The paper describes the datasets and training process but does not explicitly state the train/validation/test dataset splits used for reproduction, nor does it refer to specific predefined splits with proper citation for that split. |
| Hardware Specification | Yes | All transfer experiments were run using Tesla V100 machines. |
| Software Dependencies | No | We use the implementation in Py Torch Lightning Bolts2 (Falcon & Cho, 2020). |
| Experiment Setup | Yes | For the coarse dataset training, all models were trained for 600 epochs with an initial learning rate of 0.0003, a cosine annealing learning rate scheduler with Tmax set to 100 and the Adam W optimizer. A dropout rate of 0.05 was used. We did not use weight decay. All experiments were run using a batch size of 128 for both training and evaluation. |