On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning
Authors: Lu Han, Han-Jia Ye, De-Chuan Zhan
TMLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our method achieves steady improvement over supervised baseline and state-of-the-art performance under all class mismatch ratios on different benchmarks. ... Experiments on different SSL benchmarks empirically validate the effectiveness of our model. |
| Researcher Affiliation | Academia | Lu Han EMAIL State Key Laboratory for Novel Software Technology, Nanjing University Han-Jia Ye EMAIL State Key Laboratory for Novel Software Technology, Nanjing University De-Chuan Zhan EMAIL State Key Laboratory for Novel Software Technology, Nanjing University |
| Pseudocode | Yes | Algorithm 1 Υ-Model algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code. It only mentions 'Reviewed on Open Review: https: // openreview. net/ forum? id= t LG26Qxo D8' which is a review platform and does not host code. |
| Open Datasets | Yes | CIFAR10 (6/4) : created from CIFAR10 (Krizhevsky & Hinton, 2009). ... CIFAR100 (50/50): created from CIFAR100 (Krizhevsky & Hinton, 2009). ... Tiny Image Net (100/100): created from Tiny Image Net, which is a subset of Image Net (Deng et al., 2009) ... Image Net100 (50/50): created from the 100 class subset of Image Net (Deng et al., 2009). |
| Dataset Splits | Yes | CIFAR10 (6/4) : ... We select 400 labeled samples for each ID class and totally 20,000 unlabeled samples from ID and OOD classes. SVHN (6/4): ... We select 100 labeled samples for each ID class and totally 20,000 unlabeled samples. CIFAR100 (50/50): ... We select 100 labeled samples for each ID class and a total of 20,000 unlabeled samples. Tiny Image Net (100/100): ... We select 100 labeled samples for each ID class and 40,000 unlabeled samples. Image Net100 (50/50): ... We select 100 labeled samples for each ID class and a total of 20,000 unlabeled samples. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory used for running its experiments. It only mentions using "Wide-Res Net-28-2" as the backbone. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. It mentions "Adam as the optimization algorithm" but does not specify the version of Adam or the framework/library used (e.g., PyTorch, TensorFlow, Scikit-learn). |
| Experiment Setup | Yes | For each epoch, we iterate over the unlabeled set and random sample labeled data, each unlabeled and labeled mini-batch contains 128 samples. We adopt Adam as the optimization algorithm with the initial learning rate 3 10 3 and train for 400 epochs. ... We first train a classification model only on labeled data for 100 epochs without RPL and SEC. We update pseudo-labels every 2 epochs. For both datasets, we set τ = 0.95, γ = 0.3,K = 4. |