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Found 9 papers, 0 papers with code
YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Images
2) Small object size leads to insufficient information for effective detection.
Msmsfnet: a multi-stream and multi-scale fusion net for edge detection
In this work, we study the performance that can be achieved by state-of-the-art deep learning based edge detectors in publicly available datasets when they are trained from scratch, and devise a new network architecture, the multi-stream and multi scale fusion net (msmsfnet), for edge detection.
Self-supervised Adaptive Weighting for Cooperative Perception in V2V Communications
The performance of cooperative perception is investigated in different system settings.
Knowledge Distillation Based Semantic Communications For Multiple Users
In this paper, we consider the semantic communication (SemCom) system with multiple users, where there is a limited number of training samples and unexpected interference.
Cooperative Perception with Learning-Based V2V communications
A new late fusion scheme is proposed to leverage the robustness of intermediate features.
Subsampling Error in Stochastic Gradient Langevin Diffusions
The Stochastic Gradient Langevin Dynamics (SGLD) are popularly used to approximate Bayesian posterior distributions in statistical learning procedures with large-scale data.
Losing momentum in continuous-time stochastic optimisation
This model is a piecewise-deterministic Markov process that represents the particle movement by an underdamped dynamical system and the data subsampling through a stochastic switching of the dynamical system.
A Continuous-time Stochastic Gradient Descent Method for Continuous Data
Optimization problems with continuous data appear in, e. g., robust machine learning, functional data analysis, and variational inference.
Progressive Defense Against Adversarial Attacks for Deep Learning as a Service in Internet of Things
To reduce the computational cost without loss of generality, we present a defense strategy called a progressive defense against adversarial attacks (PDAAA) for efficiently and effectively filtering out the adversarial pixel mutations, which could mislead the neural network towards erroneous outputs, without a-priori knowledge about the attack type.
