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Found 5 papers, 1 papers with code
Feature Noise Boosts DNN Generalization under Label Noise
In this study, we introduce and theoretically demonstrate a simple feature noise method, which directly adds noise to the features of training data, can enhance the generalization of DNNs under label noise.
Low Complexity First: Duration-Centric ISI Mitigation in Molecular Communication via Diffusion
In this paper, we propose a novel inter-symbol interference (ISI) mitigation scheme for molecular communication via diffusion (MCvD) systems with the optimal detection interval.
Reconstructing Actions To Explain Deep Reinforcement Learning
Feature attribution has been a foundational building block for explaining the input feature importance in supervised learning with Deep Neural Network (DNNs), but face new challenges when applied to deep Reinforcement Learning (RL). We propose a new approach to explaining deep RL actions by defining a class of \emph{action reconstruction} functions that mimic the behavior of a network in deep RL.
Unsupervised Image Noise Modeling with Self-Consistent GAN
They jointly facilitate unsupervised learning of a noise model for various noise types.
Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation
In multi-label brain tumor segmentation, class imbalance and inter-class interference are common and challenging problems.
