Open-World Semi-Supervised Learning
Authors: Kaidi Cao, Maria Brbic, Jure Leskovec
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on image classification datasets and a single-cell annotation dataset demonstrate that ORCA consistently outperforms alternative baselines, achieving 25% improvement on seen and 96% improvement on novel classes of the Image Net dataset. |
| Researcher Affiliation | Academia | Kaidi Cao , Maria Brbi c , Jure Leskovec Department of Computer Science Stanford University {kaidicao, mbrbic, jure}@cs.stanford.edu |
| Pseudocode | Yes | Algorithm 1 ORCA: Open-wo Rld with un Certainty based Adaptive margin Require: Labeled subset Dl = {(xi, yi)}n i=1, unlabeled subset Du = {(xi)}m i=1, expected number of novel classes, a parameterized backbone fθ, linear classifier with weight W. 1: Pretrain the model parameters θ with pretext loss 2: for epoch = 1 to E do 3: u Estimate Uncertainty(Du) 4: for t = 1 to T do 5: Xl, Xu Sample Mini Batch(Dl Du) 6: Zl, Zu Forward(Xl Xu; fθ) 7: Z l, Z u Find Closest(Zl Zu) 8: Compute LP using (5) 9: Compute LS using (3) 10: Compute R using (6) 11: fθ SGD with loss LBCE + η1LCE + η2LR 12: end for 13: end for |
| Open Source Code | Yes | The code of ORCA is publicly available at https://github.com/snap-stanford/orca. |
| Open Datasets | Yes | We evaluate ORCA on four different datasets, including three standard benchmark image classification datasets CIFAR-10, CIFAR-100 (Krizhevsky, 2009) and Image Net (Russakovsky et al., 2015), and a highly unbalanced single-cell Mouse Ageing Cell Atlas dataset from biology domain (Consortium et al., 2020). |
| Dataset Splits | Yes | On all datasets, we use controllable ratios of unlabeled data and novel classes. We first divide classes into 50% seen and 50% novel classes. We then select 50% of seen classes as the labeled dataset, and the rest as unlabeled set. We show results with different ratio of seen and novel classes and with 10% labeled samples in the Appendix C. |
| Hardware Specification | Yes | Our core algorithm is developed using Py Torch (Paszke et al., 2019) and we conduct all the experiments with NVIDIA RTX 2080 Ti. |
| Software Dependencies | No | The paper mentions "Py Torch (Paszke et al., 2019)", which cites the paper introducing PyTorch, but does not provide a specific numerical version number for the PyTorch library or any other software dependency (e.g., "PyTorch 1.9"). |
| Experiment Setup | Yes | We train the model using standard SGD with a momentum of 0.9 and a weight decay of 5 10 4. The model is trained for 200 epochs with a batch size of 512. We anneal the learning rate by a factor of 10 at epoch 140 and 180. ... We set hyperparameters to the following default values: s = 10, λ = 1, η1 = 1, η2 = 1. ... We use Adam optimizer with an initial learning rate of 10 3 and a weight decay 0. The model is trained with a batch size of 512 for 20 epochs. |