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..
OpenCon: Open-world Contrastive Learning
Authors: Yiyou Sun, Yixuan Li
TMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of Open Con on challenging benchmark datasets and establish competitive performance. On the Image Net dataset, Open Con significantly outperforms the current best method by 11.9% and 7.4% on novel and overall classification accuracy, respectively. Empirically, Open Con establishes strong performance on challenging benchmark datasets, outperforming existing baselines by a significant margin (Section 5). |
| Researcher Affiliation | Academia | Yiyou Sun EMAIL Yixuan Li EMAIL University of Wisconsin-Madison |
| Pseudocode | Yes | Details of Ll and Lu are in Appendix B, along with the complete pseudo-code in Algorithm 1 (Appendix). |
| Open Source Code | Yes | The code is available at https://github.com/deeplearning-wisc/opencon. |
| Open Datasets | Yes | Datasets We evaluate on standard benchmark image classification datasets CIFAR-100 (Krizhevsky et al., 2009) and Image Net (Deng et al., 2009). |
| Dataset Splits | Yes | By default, classes are divided into 50% seen and 50% novel classes. We then select 50% of known classes as the labeled dataset, and the rest as the unlabeled set. The division is consistent with Cao et al. (2022), which allows us to compare the performance in a fair setting. |
| Hardware Specification | Yes | We run all experiments with Python 3.7 and Py Torch 1.7.1, using NVIDIA Ge Force RTX 2080Ti GPUs. |
| Software Dependencies | Yes | We run all experiments with Python 3.7 and Py Torch 1.7.1, using NVIDIA Ge Force RTX 2080Ti GPUs. |
| Experiment Setup | Yes | For CIFAR-100/Image Net-100, the model is trained for 200/120 epochs with batch-size 512 using stochastic gradient descent with momentum 0.9, and weight decay 10 4. The learning rate starts at 0.02 and decays by a factor of 10 at the 50% and the 75% training stage. The momentum for prototype updating γ is fixed at 0.9. The percentile p for OOD detection is 70%. We fix the weight for the KL-divergence regularizer to be 0.05. |