MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels
Authors: Chuanyang Hu, Shipeng Yan, Zhitong Gao, Xuming He
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our method, we perform extensive experiments on synthetic noisy datasets and real-world web data, and our strategy outperforms existing noisy-label learning methods. |
| Researcher Affiliation | Academia | Chuanyang Hu1 , Shipeng Yan1 , Zhitong Gao1 and Xuming He1,2 1Shanghai Tech University 2Shanghai Engineering Research Center of Intelligent Vision and Imaging {huchy3, yanshp, gaozht, hexm}@shanghaitech.edu.cn |
| Pseudocode | Yes | Algorithm 1 Pseudocode of Sample Selection |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for their method, nor does it include a link to a code repository. |
| Open Datasets | Yes | For synthetic noise, we follow the protocol proposed in [Kim et al., 2021] to generate symmetric and asymmetric noise on CIFAR-10 [Krizhevsky et al., 2009] and CIFAR-100 [Krizhevsky et al., 2009] datasets. For real scenarios with web noises, We adopt Mini-Imagenet, Mini-Webvision and CIFAR-N datasets for evaluation. Besides, Mini-Image Net [Jiang et al., 2020] collects noisy images from Internet. Specifically, it annotates the correctness of the collected image labels manually. The dataset includes around 50,000 training images and 5,000 val images for 100 classes. Mini-Webvision [Li et al., 2017] is derived from Webvision dataset [Li et al., 2017] by picking top 50 categories of google images. |
| Dataset Splits | Yes | The dataset includes around 50,000 training images and 5,000 val images for 100 classes. Following FINE [Kim et al., 2021], we tune the hyper-parameters by an additional validation set for CIFAR-10 and CIFAR-100. Also following Divide Mix [Li et al., 2020], the validation set is provided to tune the hyper-parameters for web noise datasets like Mini Webvision and Mini-Image Net. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments, such as particular GPU or CPU models, memory, or cloud computing instances. |
| Software Dependencies | No | The paper states, "Our implementation is based on Py Torch." However, it does not provide specific version numbers for PyTorch or any other software libraries or dependencies. |
| Experiment Setup | Yes | We adopt the SGD optimizer with an initial learning rate of 0.01, a momentum of 0.9. For Mini Webvision and Mini-Image Net, ... We use the SGD optimizer with an initial learning rate of 0.1, a momentum of 0.9. For all datasets, we use the cosine-annealing learning rate scheduler for each round and the batch size is set to 128. The training epochs of each round is 20, 50, 100, 100 for CIFAR-10, CIFAR-100, Mini-Imagenet and Mini-Webvision, respectively. |