Learning from Noisy Labels via Conditional Distributionally Robust Optimization
Authors: Hui GUO, Grace Yi, Boyu Wang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results on both synthetic and real-world datasets demonstrate the superiority of our method. |
| Researcher Affiliation | Academia | Hui Guo Department of Computer Science University of Western Ontario hguo288@uwo.ca Grace Y. Yi Department of Statistical and Actuarial Sciences Department of Computer Science University of Western Ontario gyi5@uwo.ca Boyu Wang Department of Computer Science University of Western Ontario bwang@csd.uwo.ca |
| Pseudocode | Yes | Algorithm 1: Learning from Noisy Labels via Conditional Distributionally Robust True Label Posterior with an Adaptive Lagrange multiplier (Adapt CDRP) |
| Open Source Code | Yes | Code is available at https://github.com/hguo1728/Adapt CDRP. |
| Open Datasets | Yes | We evaluate the performance of the proposed Adapt CDRP on two datasets, CIFAR-10 and CIFAR-100 [21], by generating synthetic noisy labels (details provided below), as well as four datasets, CIFAR-10N [22], CIFAR-100N [22], Label Me [23, 24], and Animal10N [25], which contain human annotations. |
| Dataset Splits | Yes | For all datasets except Label Me, we set aside 10% of the original data, together with the corresponding synthetic or human annotated noisy labels, to validate the model selection procedure. |
| Hardware Specification | Yes | Training times are approximately 3 hours on CIFAR-10 and 5.5 hours on CIFAR-100 using an NVIDIA V100 GPU. |
| Software Dependencies | No | The paper mentions the Adam optimizer but does not specify software dependencies with version numbers (e.g., PyTorch, TensorFlow, or specific library versions). |
| Experiment Setup | Yes | A batch size of 128 is maintained across all datasets. We use the Adam optimizer [43] with a weight decay of 5 10 4 for CIFAR-10, CIFAR-100, CIFAR-10N, CIFAR-100N, and Label Me datasets. The initial learning rate for CIFAR-10, CIFAR-100, CIFAR-10N, and CIFAR-100N is set to 10 3, with the networks trained for 120, 150, 120, and 150 epochs respectively. The first 30 epochs serve as a warm-up. |