Robust Visual Recognition with Class-Imbalanced Open-World Noisy Data
Authors: Na Zhao, Gim Hee Lee
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several benchmark datasets including synthetic and real-world noisy datasets demonstrate the superior performance robustness of our method over existing methods. |
| Researcher Affiliation | Academia | Na Zhao1*, Gim Hee Lee2 1Singapore University of Technology and Design 2National University of Singapore |
| Pseudocode | No | The paper describes the methods in narrative text and mathematical equations, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/Na-Z/LIOND. |
| Open Datasets | Yes | We evaluate our proposed method on three datasets, including CIFAR-10 and CIFAR-100 (Krizhevsky, Hinton et al. 2009) with controlled noise and class imbalance, and Web Vision (Li et al. 2017) that is a real-world class-imbalanced dataset with open-world noise. |
| Dataset Splits | No | The paper mentions 'training data' and 'validation sets' (e.g., in Table 4 for Web Vision), but does not explicitly provide specific percentages, sample counts, or detailed methodology for how the train/validation/test splits were created or used for reproduction across all experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. It only mentions general computing environments without specific hardware specifications. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We train the model using SGD optimizer with momentum 0.9 and weight decay 5e-4. We set the batch size as 128 and the initial learning rate as 0.02 with a cosine decay schedule. The model is trained for 300 epochs with a warmup period using Lbsce. The warmup period is set to 10 and 30 epochs for CIFAR-10 and CIFAR100, respectively. We set the hyper-parameters as α = 0.05, β = 3, γ = 0.5, K = 30, ϵl = 1, ϵh = 1, τ = 0.3, and ω = 0.99. To align with Proto Mix and NGC, we adopt Inception-Res Net V2 as the feature encoder. We train the model using SGD optimizer with momentum 0.9 and weight decay 1e-4. We set the batch size as 32 and the initial learning rate as 0.04 with a cosine decay schedule. The model is trained for 80 epochs with a 15-epoch warmup period using Lbsce. The configuration of hyper-parameters is the same as that for CIFAR datasets, except for K = 50 and ϵl = 0.1. |