Noise-Robust Learning from Multiple Unsupervised Sources of Inferred Labels
Authors: Amila Silva, Ling Luo, Shanika Karunasekera, Christopher Leckie8315-8323
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments using nine real-world datasets for three different classification tasks (images, text and graph nodes). Our results show that our approach achieves notable improvements (e.g., 6.4% in accuracy) against state-of-the-art baselines while dealing with both instance-dependent and classconditional noise in inferred label sources. |
| Researcher Affiliation | Academia | School of Computing and Information Systems The University of Melbourne Parkville, Victoria, Australia {amila.silva@student., ling.luo@, karus@, caleckie@}unimelb.edu.au |
| Pseudocode | No | The paper describes its methods through mathematical equations and figures but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or a link to its source code. It only mentions "Supplementary Material (Silva et al. 2021) provides the proofs and more details about the implementation of the loss terms". |
| Open Datasets | Yes | We select three widely-used datasets for each classification task (see Table 1). We randomly choose 75% of each dataset for training and the remaining 25% for testing. |
| Dataset Splits | No | The paper states, "We randomly choose 75% of each dataset for training and the remaining 25% for testing," but does not specify a separate validation split or strategy. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., specific GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper mentions techniques and optimizers (e.g., "Adam optimizer"), but does not specify version numbers for any software dependencies (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | After performing a grid search, we set β to 0.5 (see Fig. 5 (a)).... We adopt the Adam optimizer and set the learning rate and batch size to 0.01 and 128 respectively. |