Candidate Label Set Pruning: A Data-centric Perspective for Deep Partial-label Learning
Authors: Shuo He, Chaojie Wang, Guowu Yang, Lei Feng
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, extensive experiments on both benchmark-simulated and real-world PLL datasets validate the great value of CLSP to significantly improve many state-of-the-art deep PLL methods. |
| Researcher Affiliation | Academia | 1University of Electronic Science and Technology of China 2Nanyang Technological University |
| Pseudocode | Yes | A THE PSEUDO-CODE OF THE PROPOSED ALGORITHM Algorithm 1: The proposed CLSP method |
| Open Source Code | No | The paper mentions using open-source libraries like LAVIS and Faiss, and links to SimCLR's original configurations, but does not provide a link or explicit statement about the open-sourcing of the code for the CLSP method described in the paper. |
| Open Datasets | Yes | We use three benchmark datasets, i.e., CIFAR-10 (Krizhevsky et al., 2009), CIFAR-100 (Krizhevsky et al., 2009), Tiny-Image Net (Wu et al., 2017), and a real-world PLL dataset PASCAL VOC (Everingham et al., 2015). |
| Dataset Splits | No | The paper mentions using a 'validation set' in the context of theoretical analysis to estimate parameters, but does not provide specific details on its size or how it's split for experimental reproduction. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions using libraries like Faiss and LAVIS but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | On the whole, we employ a base training scheme: a Res Net-18 model, learning rate is 1e-2, and weight decay is 1e-3. On CIFAR-10 and CIFAR-100, CC, PRODEN, LWS, and CAVL do not employ a learning rate scheduler and the data augmentation technique which is the same as the original implementation. But, on more difficult datasets CIFAR-10-LT, CIFAR-100-LT, Tiny-Image Net, and VOC, they are equipped with a consistency regularization with augmented examples and a Cosine Annealing Learning Rate scheduler... Especially, on VOC, the epoch is set to 100 for all PLL methods to avoid overfitting. |