Dataset pruning aims to construct a coreset capable of achieving performance comparable
to the original full dataset. Most existing dataset pruning methods rely on snapshot-based …

Coreset selection is powerful in reducing computational costs and accelerating data
processing for deep learning algorithms. It strives to identify a small subset from large-scale …

Mitigating hallucinations of Large Multi-modal Models (LMMs) is crucial to enhance their
reliability for general-purpose assistants. This paper shows that such hallucinations of LMMs …

Coreset selection is powerful in reducing computational costs and accelerating data
processing for deep learning algorithms. It strives to identify a small subset from large-scale …

Fatigue is a significant safety concern across various domains, and accurate detection is
vital. However, the commonly employed fine-grained labels (seconds-based) frequently …

The expanding model size and computation of deep neural networks (DNNs) have
increased the demand for efficient model deployment methods. Quantization-aware training …

Learning with Noisy labels (LNL) poses a significant challenge for the Machine Learning
community. Some of the most widely used approaches that select as clean samples for …

Process uncertainty has a significant impact on industrial image processing. Existing deep
learning methods were established on high-quality datasets without considering the …

Training high-quality deep models necessitates vast amounts of data, resulting in
overwhelming computational and memory demands. Recently, data pruning, distillation, and …

Selecting key data subsets for model training is an effective way to improve training
efficiency. Existing methods generally utilize a well-trained model to evaluate samples and …