OpenCon: Open-world Contrastive Learning
Authors: Yiyou Sun, Yixuan Li
TMLR 2023 | Venue PDF | Archive PDF | Plain Text | 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. |