Deep learning technology has developed unprecedentedly in the last decade and has
become the primary choice in many application domains. This progress is mainly attributed …

Large-scale, high-quality data are considered an essential factor for the successful
application of many deep learning techniques. Meanwhile, numerous real-world deep …

Dataset distillation (DD) is a newly emerging research area aiming at alleviating the heavy
computational load in training models on large datasets. It tries to distill a large dataset into a …

Sparse training has emerged as a promising method for resource-efficient deep neural
networks (DNNs) in real-world applications. However, the reliability of sparse models …

Dataset distillation (DD) is an increasingly important technique that focuses on constructing
a synthetic dataset capable of capturing the core information in training data to achieve …

While deep learning techniques have proven successful in image-related tasks, the
exponentially increased data storage and computation costs become a significant …

This paper presents our method for the generative track of The First Dataset Distillation
Challenge at ECCV 2024. Since the diffusion model has become the mainstay of generative …

Knowledge Distillation (KD) has evolved into a practical technology for transferring
knowledge from a well-performing model (teacher) to a weak model (student). A counter …

In spite of delivering remarkable predictive accuracy across many domains, including
computer vision and medical imaging, Deep Neural Networks (DNNs) are susceptible to …

While modern deep neural networks have achieved remarkable success, they have
exhibited a notable deficiency in reliably estimating uncertainty. Many existing studies …