Confidence-based Reliable Learning under Dual Noises
Authors: Peng Cui, Yang Yue, Zhijie Deng, Jun Zhu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on various challenging synthetic and real-world noisy datasets verify that the proposed method can outperform competing baselines in the aspect of classification performance. |
| Researcher Affiliation | Collaboration | Peng Cui1 3, Yang Yue1, Zhijie Deng1 2 , Jun Zhu1 1 Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center, Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University, Beijing, 100084 China 2 Qing Yuan Research Institute, Shanghai Jiao Tong University 3 Real AI |
| Pseudocode | Yes | Algorithm 1: Training DNNs under (x,y)-noise |
| Open Source Code | Yes | 3.a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | The proposed method is first evaluated on two benchmark datasets with synthetic noise: CIFAR-100 [24] and Tiny Image Net [24] (the subset of Image Net[9])... Moreover, we validate the effectiveness of the proposed method under more challenging real-world noise on Web Vision [28]. |
| Dataset Splits | Yes | Table 1 presents the results of all methods on CIFAR-100 and Tiny Image Net with different rates of x-noise and y-noise. ... Table 2 lists the experimental results. As we can see, the proposed method significantly outperforms other baselines not only on the Web Vision validation set but also on the ILSVRC12 validation set [9]. |
| Hardware Specification | No | The paper mentions support from the 'High Performance Computing Center, Tsinghua University' in the Acknowledgement section, but this is a general reference and does not specify any particular GPU models, CPU types, or other detailed hardware specifications used for the experiments. |
| Software Dependencies | No | The paper states that 'SGD is used to optimize the network' and that 'The deep ensemble we used consists of 5 Res Net18', but it does not specify any software versions for libraries (e.g., TensorFlow, PyTorch, scikit-learn) or programming languages. |
| Experiment Setup | Yes | SGD is used to optimize the network with a batch size of 256. More details can be found in Appendix B. |