Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
Authors: Tao Han, Junyu Gao, Yuan Yuan, Qi Wang
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and analysis across four standard semi-supervised learning benchmarks validate that USADTM achieves top performance (e.g., 90.46% accuracy on CIFAR-10 with 40 labels and 95.20% accuracy with 250 labels). |
| Researcher Affiliation | Academia | Tao Han , Junyu Gao , Yuan Yuan and Qi Wang School of Computer Science and Center for OPTical IMagery Analysis and Learning Northwestern Polytechnical University Xi an, Shaanxi, P.R. China. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is released at https://github.com/taohan10200/USADTM. |
| Open Datasets | Yes | CIFAR-10 and CIFAR-100 [18] are large datasets of tiny RGB images with size 32x32. (...) STL-10 [38] is dataset designed for unsupervised and semi-supervised learning. (...) SVHN [48] Dateset is derived from Google Street View House Number. |
| Dataset Splits | Yes | CIFAR-10 and CIFAR-100 (...) Both sets provide 50,000 training labels and 10,000 validation labels. STL-10 (...) consists of 5,000 training labels and 8,000 valdiation labels. |
| Hardware Specification | Yes | The training and evaluation are performed on NVIDIA GTX 1080Ti GPU. |
| Software Dependencies | No | The paper mentions using a 'Py Torch version of the Fix Match framework [49]' but does not provide specific version numbers for PyTorch or other software libraries. |
| Experiment Setup | Yes | The setting of batch size for labeled data and unlabeled data follows [22]. The SGD algorithm with 0.03 initialization learning rate is adopted to optimize the network. (...) T-MI loss will not join the training until after some epochs. (...) The purpose of setting the threshold τ is to filter out part of the wrong allocation. In the absence of special instructions, τ is generally set at 0.85. (...) K = len(Xl)/C * 2. (...) α denotes the weighting parameter of mutual information loss. (...) The recommended setting is 0.1. |