SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning
Authors: Haobo Wang, Mingxuan Xia, Yixuan Li, Yuren Mao, Lei Feng, Gang Chen, Junbo Zhao
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, So Lar exhibits substantially superior results on standardized benchmarks compared to the previous state-of-the-art PLL methods. |
| Researcher Affiliation | Academia | 1Key Lab of Intelligent Computing based Big Data of Zhejiang Province, Zhejiang University 2School of Software Technology, Zhejiang University 3Department of Computer Sciences, University of Wisconsin-Madison 4College of Computer Science, Chongqing University 5Center for Advanced Intelligence Project, RIKEN |
| Pseudocode | Yes | Algorithm 1: Pseudo-code of So Lar. |
| Open Source Code | Yes | Code and data are available at: https://github.com/hbzju/So Lar. |
| Open Datasets | Yes | First, we evaluate So Lar on two long-tailed datasets CIFAR10-LT and CIFAR100-LT introduced in [20, 21]. ... we conduct experiments on the large-scale SUN397 dataset [27] |
| Dataset Splits | Yes | The training images are randomly removed class-wise to follow a pre-defined imbalance ratio γ = n1 n L , where nj is the image number of the j-th class. ... For the SUN397 dataset, we hold out 50 samples per class for testing |
| Hardware Specification | Yes | We train all models on 8 NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper mentions using ResNet, SGD optimizer, consistency regularization [19], and Mixup [25] techniques, but does not provide specific version numbers for any software libraries or dependencies (e.g., PyTorch version, TensorFlow version, etc.). |
| Experiment Setup | Yes | The model is trained for 1000 epochs using a standard SGD optimizer with a momentum of 0.9. The initial learning rate is set as 0.01, and decays by the cosine learning rate schedule. The batch size is 256. ... For our Sinkhorn-Knopp algorithm, we fix the smoothing regularization parameter as λ = 3 and the length of the queue for acceleration as 64 times batch size. The moving-average parameter µ for class prior estimation is set as 0.1/0.05 in the first stage and fixed as 0.01 later. For class-wise reliable sample selection, we linearly ramp up ρ from 0.2 to 0.5/0.6 in the first 50 epochs and fix the high-confidence selection threshold τ as 0.99. |