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..
Instance-dependent Label-noise Learning under a Structural Causal Model
Authors: Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, the proposed method outperforms all state-of-the-art methods on both synthetic and real-world label-noise datasets. In Section 4, we compare the classification accuracy of proposed method with popular label-noise learning algorithms [15, 19, 11, 10, 28, 36, 16] on both synthetic and real-world datasets. |
| Researcher Affiliation | Collaboration | Yu Yao1 Tongliang Liu1 Mingming Gong2 Bo Han3 Gang Niu4 Kun Zhang5 1TML Lab, University of Sydney; 2University of Melbourne; 3Hong Kong Baptist University; 4RIKEN AIP; 5Carnegie Mellon University |
| Pseudocode | Yes | Algorithm 1 Causal NL |
| Open Source Code | No | The paper does not provide an explicit statement or link to its open-source code. |
| Open Datasets | Yes | We examine the efficacy of our approach on manually corrupted versions of four datasets, i.e., Fashion MNIST [31], SVHN [18], CIFAR10, CIFAR100 [13], and one real-world noisy dataset, i.e., Clothing1M [32]. |
| Dataset Splits | No | The paper mentions '50,000 training images and 10,000 test images' for CIFAR10/100, but does not explicitly state training/validation/test splits, or reference predefined splits for reproducibility. |
| Hardware Specification | Yes | For a fair comparison, all experiments are conducted on NVIDIA Tesla V100, and all methods are implemented by Py Torch. |
| Software Dependencies | No | The paper mentions 'all methods are implemented by Py Torch' but does not provide specific version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | Dimension of the latent representation Z is set to 25 for all synthetic noisy datasets. For encoder networks ˆqφ1 1(X) and ˆqφ2 1(X), we use the same network structures with the baseline method. Specially, we use a Res Net-18 network for Fashion MNIST, a Res Net-34 network for SVHN and CIFAR10, a Res Net-50 network for CIFAR100 without pretraining. For Clothing1M, we use Res Net-50 networks pre-trained on Image Net. The data-augmentation methods random crop and horizontal flip are used for our method. For Clothing1M, we use a Res Net-50 network pre-trained on Image Net, and the clean training data is not used. The dimensionality of the latent representation Z is set to 100. |