Deep Learning
Deep learning, a subfield of machine learning, focuses on training artificial neural networks with multiple layers to extract complex patterns from data. Current research emphasizes improving model robustness against noisy or adversarial inputs, exploring efficient architectures like Vision Transformers and convolutional LSTMs for various tasks (e.g., image classification, time series forecasting), and integrating physics-informed approaches for enhanced interpretability and reliability. These advancements are significantly impacting diverse fields, from automated industrial inspection and medical image analysis to improved weather forecasting and more efficient content moderation systems.
Papers
Weight-Sharing Regularization
Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien
Discret2Di -- Deep Learning based Discretization for Model-based Diagnosis
Lukas Moddemann, Henrik Sebastian Steude, Alexander Diedrich, Oliver Niggemann
Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination
Peng Wang, Xiao Li, Can Yaras, Zhihui Zhu, Laura Balzano, Wei Hu, Qing Qu
Monocular UAV Localisation with Deep Learning and Uncertainty Propagation
Xueyan Oh, Ryan Lim, Leonard Loh, Chee How Tan, Shaohui Foong, U-Xuan Tan
TFNet: Tuning Fork Network with Neighborhood Pixel Aggregation for Improved Building Footprint Extraction
Muhammad Ahmad Waseem, Muhammad Tahir, Zubair Khalid, Momin Uppal
Newvision: application for helping blind people using deep learning
Kumar Srinivas Bobba, Kartheeban K, Vamsi Krishna Sai Boddu, Vijaya Mani Surendra Bolla, Dinesh Bugga
Uncertainty Quantification of Deep Learning for Spatiotemporal Data: Challenges and Opportunities
Wenchong He, Zhe Jiang
Potato Leaf Disease Classification using Deep Learning: A Convolutional Neural Network Approach
Utkarsh Yashwant Tambe, A. Shobanadevi, A. Shanthini, Hsiu-Chun Hsu
Thermal Face Image Classification using Deep Learning Techniques
Prosenjit Chatterjee, ANK Zaman
Exploring Deep Learning Techniques for Glaucoma Detection: A Comprehensive Review
Aized Amin Soofi, Fazal-e-Amin
Exploring Deep Learning Image Super-Resolution for Iris Recognition
Eduardo Ribeiro, Andreas Uhl, Fernando Alonso-Fernandez, Reuben A. Farrugia
Deep Learning for real-time neural decoding of grasp
Paolo Viviani, Ilaria Gesmundo, Elios Ghinato, Andres Agudelo-Toro, Chiara Vercellino, Giacomo Vitali, Letizia Bergamasco, Alberto Scionti, Marco Ghislieri, Valentina Agostini, Olivier Terzo, Hansjörg Scherberger
GIST: Generated Inputs Sets Transferability in Deep Learning
Florian Tambon, Foutse Khomh, Giuliano Antoniol
Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives
Enhao Ning, Changshuo Wang, Huang Zhangc, Xin Ning, Prayag Tiwari
A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma based on CT Images
Ni Yao, Hang Hu, Kaicong Chen, Chen Zhao, Yuan Guo, Boya Li, Jiaofen Nan, Yanting Li, Chuang Han, Fubao Zhu, Weihua Zhou, Li Tian
Space Narrative: Generating Images and 3D Scenes of Chinese Garden from Text using Deep Learning
Jiaxi Shi1, Hao Hua1
Walnut Detection Through Deep Learning Enhanced by Multispectral Synthetic Images
Kaiming Fu, Tong Lei, Maryia Halubok, Brian N. Bailey