Deep Learning Theory and Applications: Third International Conference, DeLTA 2022, Lisbon, Portugal, July 12–14, 2022, Revised Selected Papers (Communications in Computer and Information Science)

✍ Scribed by Ana Fred (editor), Carlo Sansone (editor), Oleg Gusikhin (editor), Kurosh Madani (editor)

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 130
• Category: Library

✦ Synopsis

This book constitutes the refereed post-conference proceedings of the Third International Conference on Deep Learning Theory and Applications, DeLTA 2022, held in Lisbon, Portugal, during January 17-18, 2022.

The 6 full papers included in this book were carefully reviewed and selected from 36 submissions. They present recent research on machine learning and artificial intelligence in real-world applications such as computer vision, information retrieval and summarization from structured and unstructured multimodal data sources, natural language understanding and translation, and many other application domains.

✦ Table of Contents

Preface
Organization
Contents
Modified SkipGram Negative Sampling Model for Faster Convergence of Graph Embedding
1 Introduction
2 Related Work
3 Graph Embedding: Random Walk Based Technique
3.1 Standard SkipGram Model: Negative Sampling Approach
3.2 Proposed SkipGram Model
3.3 Community Detection and Link Prediction Tasks
4 Experimental Evaluation
4.1 Data Description and Implementation Details
4.2 Evaluation of DeepWalkb Model
5 Conclusions
References
Active Collection of Well-Being and Health Data in Mobile Devices
1 Introduction
2 Models for Smart Notification and Users
2.1 Reinforcement Learning Algorithms
2.2 State Representation
2.3 Reward Definition
2.4 Environment Model - Simulation
3 Experimental Description and Metrics
3.1 Model Initialization Methods
3.2 Daily vs. Weekly Routine
3.3 Performance Metrics
3.4 Implementation of the Smart Notification App
4 Results and Discussion
4.1 Simulation Results
4.2 Real-World Pilot
5 Conclusions and Future Work
References
Reliable Classification of Images by Calculating Their Credibility Using a Layer-Wise Activation Cluster Analysis of CNNs
1 Introduction
2 Related Work
3 Method
3.1 Identifying Clusters in Layer Activations
3.2 Obtaining In-Distribution Statistics
3.3 Calculating the Credibility of an Image at Inference
4 Experiments
4.1 Baseline
4.2 Testing with Fewer Layers
4.3 Testing on ImageNet Data
5 Conclusion
References
Traffic Sign Repositories: Bridging the Gap Between Real and Synthetic Data
1 Introduction
2 Synthetic vs. Real Traffic Sign Datasets
3 Related Work
3.1 Traffic Sign Datasets
3.2 Synthetic Traffic Signs
4 Synthetic Traffic Signs Generation Algorithm
4.1 Background
4.2 Brightness Distribution
4.3 Confetti Noise
4.4 Perlin Noise
4.5 Synthetic Generation Pipeline
5 Evaluation
5.1 Solo Synthetic Dataset Evaluation
5.2 Combining Real and Synthetic Data
5.3 Cross-Testing
5.4 Unleashing Synthetic Datasets
6 Conclusion
References
Convolutional Neural Networks for Structural Damage Localization on Digital Twins
1 Introduction and Background
2 Methodology and Methods
2.1 Digital Twin Design
2.2 Deep Learning for the Damage Localization
3 Case Study
4 Experiments and Results
4.1 Damage Localization via CNN
4.2 Noise Tolerance Evaluation
4.3 Random Search Algorithm for Hyperparameter Optimization
5 Conclusion
References
Evaluating and Improving RoSELS for Road Surface Extraction from 3D Automotive LiDAR Point Cloud Sequences*-4pt
1 Introduction
2 Related Work
2.1 Road Edge Detection
2.2 Scene Classification
2.3 Ground Plane Estimation
2.4 Surface Reconstruction
3 Background: RoSELS System Description
3.1 Ground Point Detection (S1)
3.2 Road Edge Point Detection (S2a)
3.3 Frame Classification (S2b)
3.4 Edge Point Set Smoothing (S3)
3.5 3D Road Surface Extraction (S4)
4 Evaluating and Improving RoSELS
5 Experiments and Results
5.1 Implementation of Road Surface Extraction
5.2 Experiments and Results
6 Conclusions
References
Author Index