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 | Conference PDF | Archive PDF | Plain Text | 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. |