Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Exploiting Class Activation Value for Partial-Label Learning
Authors: Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various datasets demonstrate that our proposed CAVL method achieves stateof-the-art performance. In this section, extensive experiments on various datasets are implemented to verify the effectiveness and rightness of our proposed CAV and CAVL method. |
| Researcher Affiliation | Collaboration | 1Shanghai Jiao Tong University 2Hong Kong Baptist University 3Chongqing University 4The University of Sydney 5RIKEN 6Microsoft Research Asia 7The University of Tokyo |
| Pseudocode | Yes | Algorithm 1 CAVL Algorithm |
| Open Source Code | No | The paper states 'This work is partially supported by Huawei Mind Spore (Huawei, 2020).' and provides a URL 'https://www.mindspore.cn/'. This refers to a general platform, not the specific source code for the methodology described in the paper. No other explicit statement or link providing access to their source code was found. |
| Open Datasets | Yes | We use four popular benchmark datasets to test the performance of our CAVL, which are MNIST (Le Cun et al., 1998), Fashion-MNIST (Xiao et al., 2017), Kuzushiji MNIST (Clanuwat et al., 2018) and CIFAR-10 (Krizhevsky et al., 2009). |
| Dataset Splits | Yes | Hyper-parameters are searched to maximize the accuracy on a validation set containing 10% of the training samples annotated by true labels. |
| Hardware Specification | Yes | All the implemented methods are trained on 1 RTX3090 GPU with 24 GB memory. |
| Software Dependencies | No | The paper mentions 'Huawei Mind Spore (Huawei, 2020)' as a supporting framework, but does not provide specific version numbers for it or any other software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | For all methods we search the initial learning rate from {0.0001, 0.001, 0.01, 0.05, 0.1, 0.5}, and weight decay from {10-6, 10-5, ..., 10-1}. We take a mini-batch size of {32, 256} images and train all the methods using Adam (Kingma & Ba, 2015) optimizer for 250 epochs. |