An Information Fusion Approach to Learning with Instance-Dependent Label Noise
Authors: Zhimeng Jiang, Kaixiong Zhou, Zirui Liu, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations on synthetic and real-world datasets demonstrate that our method is superior to the state-of-the-art approaches, and achieves more stable training for instance-dependent label noise. |
| Researcher Affiliation | Collaboration | Zhimeng Jiang1, Kaixiong Zhou2, Zirui Liu2, Li Li3, Rui Chen3, Soo-Hyun Choi4 , Xia Hu2 1Texas A&M University, 2Rice University, 3Samsung Research America, 4Samsung Electronics |
| Pseudocode | Yes | Algorithm 1: Information Fusion Algorithm Input :Noisy dataset D = (xn, yn)N n=1; Noisy validation data; tolerant epochs t. Output :The robust neural network over noisy label. Algorithm 2: Instance-Dependent Label Noise Generation Input :Clean samples (xn, yn)N n=1; Overall noise rate τ; Number of classes c; Size of input features: 1 d. Output :Noisy samples (xi, yn)N n=1. |
| Open Source Code | No | The paper does not provide a concrete access link or explicit statement about the release of its source code. |
| Open Datasets | Yes | Datasets. We verify the superiority of IF on three manually corrupted datasets, i.e., F-MNIST, SVHN, CIFAR-10, and one real-world noisy dataset Clothing1M. The first three datasets contain clean data, and we manually corrupt the labels of the training datasets by following (Xia et al., 2020). IDN-τ means that the controlled noise rate is τ. All experiments on those datasets with synthetic instance-dependent label noise are repeated five times. The real-world dataset Clothing1M has 1M images with real-world noisy labels and 10k images with clean labels for testing. |
| Dataset Splits | Yes | In the experiments, we leave out 10% of the noisy training samples as a noisy validation set for model selection. |
| Hardware Specification | Yes | All baselines and IF approaches are implemented in Py Torch, and tested on a machine with AMD EPYC 7282 16-core processors, 4 Ge Force GTX-3090 Ti GPUs with 24GB memory size. |
| Software Dependencies | No | The paper mentions that the methods are "implemented in Py Torch" but does not specify the version number of PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Specifically, we use a Res Net-18 network for F-MNIST, a Res Net-34 network for SVHN and CIFAR-10. For the optimization, we first use SGD with 0.9 momentum, 10 4 weight decay, 128 batch size, 50 epochs and an initial learning rate of 10 2 to initialize the network. Then, we adopt Adam optimizer and 5 10 7 learning rate to learn the NTM following PTD (Xia et al., 2020). |