DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples
Authors: Yi Xu, Jiandong Ding, Lu Zhang, Shuigeng Zhou
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four standard SSL benchmarks show that DP-SSL can provide reliable labels for unlabeled data and achieve better classification performance on test sets than existing SSL methods, especially when only a small number of labeled samples are available. Concretely, for CIFAR-10 with only 40 labeled samples, DP-SSL achieves 93.82% annotation accuracy on unlabeled data and 93.46% classification accuracy on test data, which are higher than the SOTA results. |
| Researcher Affiliation | Collaboration | Yi Xu1 Jiandong Ding2 Lu Zhang1 Shuigeng Zhou 1 1Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, China 2Alibaba Group |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statement or link indicating that the authors are releasing the source code for their method. |
| Open Datasets | Yes | We compare DP-SSL with major existing methods on CIFAR-10 [53], CIFAR-100 [53], SVHN [54] and STL-10 [55]. |
| Dataset Splits | Yes | CIFAR-10 and CIFAR-100 [53] contain 50,000 training examples and 10,000 validation examples. SVHN [54] is a digital image dataset that consists of 73,257, 26,032 and 531,131 samples in the train, test, and extra folders. STL-10 [55]...consists of 5000 labeled images and 8000 validation samples of 96x96 size from 10 classes. |
| Hardware Specification | Yes | All experiments are implemented in Pytorch v1.7 and conducted on 16 NVIDIA RTX3090s. |
| Software Dependencies | Yes | All experiments are implemented in Pytorch v1.7 and conducted on 16 NVIDIA RTX3090s. |
| Experiment Setup | Yes | In our framework, the batch size for labeled data and unlabeled data is set to 64 and 448, respectively. Besides, we use the same hyperparameters (K = 50, ρ = 0.2, ϵ = 0.95) for all datasets. |