Review of Non-Invasive Glioma Grading Research: Lightweight Convolutional Neural Networks Based on Knowledge Distillation Background Gliomas are the main tumors of the central nervous system, and early detection is crucial. The World Health Organization (WHO) classifies gliomas from grade I to IV, with grades I and II being low-grade gliomas (LGG) ...

In recent years, applications such as privacy protection and large-scale data transmission have posed significant challenges to the inaccessibility of data. Researchers have proposed Data-Free Knowledge Distillation (DFKD) methods to address these issues. Knowledge Distillation (KD) is a method for training a lightweight model (student model) to le...

Application of a Prototype-Based Sample Weighted Distillation Unified Framework in Missing Modality Sentiment Analysis Research Background Sentiment analysis is a significant field in Natural Language Processing (NLP). With the development of social media platforms, people increasingly tend to express their emotions through short video clips. This ...

Solving Few-Shot Classification Problems with Balanced Feature Alignment and Uniformity Background and Motivation The goal of Few-Shot Learning (FSL) is to correctly recognize new samples with only a few examples from new classes. Existing few-shot learning methods mainly learn transferable knowledge from base classes by maximizing the information ...

Weakly Supervised Semantic Image Segmentation via Alternate Self-Dual Teaching Background Introduction With the continuous development of the computer vision field, semantic segmentation has become an important and active research direction. Traditional semantic segmentation methods rely on manually labeled pixel-level tags; however, obtaining thes...