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Found 6 papers, 1 papers with code
Challenges for Predictive Modeling with Neural Network Techniques using Error-Prone Dietary Intake Data
In this work, we demonstrate the ways in which measurement error erodes the performance of neural networks, and illustrate the care that is required for leveraging these models in the presence of error.
Memory-Efficient Continual Learning Object Segmentation for Long Video
We propose two novel techniques to reduce the memory requirement of Online VOS methods while improving modeling accuracy and generalization on long videos.
CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning
Continual learning in real-world scenarios is a major challenge.
Deep Neural Network Perception Models and Robust Autonomous Driving Systems
This paper analyzes the robustness of deep learning models in autonomous driving applications and discusses the practical solutions to address that.
Potential adversarial samples for white-box attacks
Deep convolutional neural networks can be highly vulnerable to small perturbations of their inputs, potentially a major issue or limitation on system robustness when using deep networks as classifiers.
Unsupervised Feature Learning Toward a Real-time Vehicle Make and Model Recognition
Here, we formulate the vehicle make and model recognition as a fine-grained classification problem and propose a new configurable on-road vehicle make and model recognition framework.
