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..
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
Authors: Hao Cheng, Zhaowei Zhu, Xingyu Li, Yifei Gong, Xing Sun, Yang Liu
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of CORES2 on CIFAR10 and CIFAR100 datasets with synthetic instance-dependent label noise and Clothing1M with real-world human noise. ... CORES2 achieves competitive performance on multiple datasets, including CIFAR-10, CIFAR-100, and Clothing1M, under different label noise settings. |
| Researcher Affiliation | Collaboration | University of California, Santa Cruz, Tencent You Tu Lab EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Instance-Dependent Label Noise Generation |
| Open Source Code | Yes | Code is available at https://github.com/UCSC-REAL/cores. |
| Open Datasets | Yes | Datasets: CORES2 is evaluated on three benchmark datasets: CIFAR-10, CIFAR-100 (Krizhevsky et al., 2009) and Clothing1M (Xiao et al., 2015). |
| Dataset Splits | Yes | Standard data augmentation is applied to each dataset. ... We use Res Net34 for CIFAR-10 and CIFAR-100 and Res Net50 for Clothing1M. |
| Hardware Specification | No | Only mentions using Res Net34 for CIFAR-10 and CIFAR-100 and Res Net50 for Clothing1M, which are model architectures, not hardware specifications for running experiments. No details on GPUs, CPUs, or memory were provided. |
| Software Dependencies | No | The paper mentions optimizer (SGD) and loss functions (CE, KL-divergence) but does not provide specific software library names with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | The basic hyper-parameters settings for CIFAR-10 and CIFAR-100 are listed as follows: mini-batch size (64), optimizer (SGD), initial learning rate (0.1), momentum (0.9), weight decay (0.0005), number of epochs (100) and learning rate decay (0.1 at 50 epochs). |