Mitigating Label Noise through Data Ambiguation
Authors: Julian Lienen, Eyke Hüllermeier
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In an extensive empirical evaluation, our method demonstrates favorable learning behavior on synthetic and real-world noise, confirming the effectiveness in detecting and correcting erroneous training labels. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Paderborn University 2Institute of Informatics, LMU Munich 3Munich Center for Machine Learning julian.lienen@upb.de, eyke@lmu.de |
| Pseudocode | Yes | Algorithm 1 Robust Data Ambiguation (RDA) Loss |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository links, explicit statements of code release) for the source code of the methodology described. |
| Open Datasets | Yes | Here, we consider CIFAR-10/-100 (Krizhevsky and Hinton 2009) as well as the large-scale datasets Web Vision (Li et al. 2017) and Clothing1M (Xiao et al. 2015) as benchmarks. |
| Dataset Splits | No | The paper mentions 'hyperparameters and their optimization' which are said to be in the appendix, implying a validation process, but it does not explicitly state specific dataset splits (e.g., percentages, counts) for training, validation, or testing in the main text. It only explicitly mentions 'test splits'. |
| Hardware Specification | No | The paper states that 'A more thorough overview of experimental details, such as hyperparameters and their optimization, as well as the technical infrastructure, can be found in the appendix.' However, the main text does not provide specific hardware details (e.g., GPU models, CPU types) used for the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiments. It mentions 'technical infrastructure' is in the appendix, but no details are in the main text. |
| Experiment Setup | No | The paper states that 'A more thorough overview of experimental details, such as hyperparameters and their optimization, as well as the technical infrastructure, can be found in the appendix.' The main text itself does not contain specific experimental setup details like concrete hyperparameter values or training configurations. |