5 papers
Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining
Yingbin Liang, Lu Dai, Shuo Shi +3
Complex data mining has wide application value in many fields, especially in the feature extraction and classification tasks of unlabeled data. This paper proposes an algorithm bas…
Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems
You Wu, Mengfang Sun, Hongye Zheng +3
This document presents an in-depth examination of stock market sentiment through the integration of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU), enabling pr…
Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data
Aoran Shen, Minghao Dai, Jiacheng Hu +3
In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional…
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches
Yinqiu Feng, Aoran Shen, Jiacheng Hu +3
This paper presents an innovative approach to enhancing few-shot learning by integrating data augmentation with model fine-tuning in a framework designed to tackle the challenges p…
Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm
Guanming Huang, Aoran Shen, Yuxiang Hu +3
This paper explores the application of knowledge distillation technology in target detection tasks, especially the impact of different distillation temperatures on the performance…