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..
The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning
Authors: Zhenmei Shi, Jiefeng Chen, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, Somesh Jha
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our analysis and method empirically with systematic experiments using real-world datasets and foundation models. |
| Researcher Affiliation | Collaboration | 1 University of Wisconsin-Madison 2 Google LLC 3 Xai Pient Equal contribution EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods and equations, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Please refer to our released code1 for more details. 1https://github.com/zhmeishi/trade-off_contrastive_learning |
| Open Datasets | Yes | CIFAR-10 (Krizhevsky et al., 2009) dataset consists of 60,000 32 32 color images in 10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck. Each class has 6,000 images. There are 50,000 training images and 10,000 test images. |
| Dataset Splits | Yes | There are 50,000 training images and 10,000 test images. ... Then we fix the pre-trained feature extractor and train a linear classifier (Linear Probing, LP) on 1%, 5%, 10%, 20%, 100% of the labeled data from the downstream task. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions optimizers (SGD, Adam W) and learning rate schedulers, but does not provide specific software names with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | We pre-train a Res Net18 network (He et al., 2016) as a feature extractor under different contrastive learning methods using SGD for 800 epochs with a cosine learning-rate scheduler, the base learning rate of 0.06, weight decay 5e-4, momentum 0.9 and batch size 512. Then we fix the pre-trained feature extractor and train a linear classifier (Linear Probing, LP) on 1%, 5%, 10%, 20%, 100% of the labeled data from the downstream task. For LP we use SGD for 200 epochs with a cosine learning-rate scheduler, the base learning rate of 5.0, no weight decay, momentum 0.9, and batch size 256. |