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 …

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

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 …

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 …

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

In this work, we consider whether pretraining on a pruned high-quality subset of a large-
scale text dataset can improve LLM performance. While existing work has shown that …

In this dissertation, we propose a systemic framework that prioritizes informative features
and examples to enhance each stage of the development process. Specifically, we prioritize …

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

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 …

With high-quality annotation data, edge AI has emerged as a pivotal technology in various
domains. Unfortunately, due to sensor errors and discrepancies in data collection, datasets …