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..
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
Authors: Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, Jiashi Feng
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on image classification and semantic segmentation verify the effectiveness of Core-tuning. |
| Researcher Affiliation | Collaboration | Yifan Zhang1 Bryan Hooi1 Dapeng Hu1 Jian Liang2 Jiashi Feng3 1National University of Singapore 2Chinese Academy of Sciences 3SEA AI Lab |
| Pseudocode | Yes | The pseudo code is provided in the supplementary. |
| Open Source Code | Yes | The source code of Core-tuning is available at: https://github.com/Vanint/Core-tuning. |
| Open Datasets | Yes | Image Net20 (a subset of Image Net with 20 classes), CIFAR10, CIFAR100 [29], Caltech-101 [15], DTD [10], FGVC Aircraft [39], Standard Cars [28], Oxford-IIIT Pets [44] and Oxford 102 Flowers [42]. |
| Dataset Splits | Yes | evaluated on val2012 set. |
| Hardware Specification | No | The paper mentions 'SGD based on two GPUs' but does not specify the type or model of GPUs or any other hardware components used for experiments. |
| Software Dependencies | No | The paper states 'We implement Core-tuning in Py Torch' but does not provide specific version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | Following [6], we perform parameter tuning for η and α from {0.1, 1, 10} on each dataset. Moreover, we set the temperature τ=0.07. To make the generated negative pairs closer to negatives, we clip λ Beta(α, α) by λ λn when generating hard negative pairs, where λn is a threshold and we set it to 0.8. |