Learning from Difficult Data

First International Workshop, LDD 2025, Bologna, Italy, October 25–30, 2025, Revised Selected Papers
Erscheinungsjahr 2027
ISBN: 978-3-032-44239-0
Verlag: Springer

Buch, Englisch, 82 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Communications in Computer and Information Science

This book constitutes the revised selected papers of the first International Workshop on Learning from Difficult Data, LDD 2025, held in Bologna, Italy, during October 25–30, 2025.

The 6 full papers included in this volume were carefully reviewed and selected out of 12 submissions. The papers cover the broad area of building pattern recognition systems for diffcult data analysis. 

Zielgruppe

Research

Autoren/Hrsg.

Zyblewski, Pawel

Wozniak, Michal

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

.- Workshop on Learning from Difficult Data
.- Multi-view mid fusion: a universal approach for learning in an HDLSS setting.
.- Adversarial Training for Suppressing Eye Disease Damage Bias in Retinal Image Quality Assessment.
.- You’ve got to keep trying: A Case Study on Tackle Detection with Limited Repurposed Data.
.- Visual Prompting for Object Counting: A Hybrid Approach for Low-Data Scenarios.
.- Optimizing Small Transformer-Based Language Models for Multi-Label Sentiment Analysis in Short Texts.
.- eMargin: Revisiting Contrastive Learning with Margin-Based Separation.