Results 51 to 60 of about 788,437 (264)
Learning Residual Images for Face Attribute Manipulation
, 2017 Face attributes are interesting due to their detailed description of human faces. Unlike prior researches working on attribute prediction, we address an inverse and more challenging problem called face attribute manipulation which aims at modifying a ...
Liu, Rujie, Shen, Wei
core +1 more source
A Multi-Task Learning for Submarine Cable Magnetic Anomaly Recognition
Journal of Marine Science and Engineering, 2023 The recognition of submarine cable magnetic anomaly (SCMA) signals is a challenging task in magnetic signal data processing. In this study, a multi-task convolutional neural network (MTCNN) model is proposed to simultaneously recognize abnormal signals ...
Yutao Liu +8 more
doaj +1 more source
Learning Residual Finite-State Automata Using Observation Tables
, 2010 We define a two-step learner for RFSAs based on an observation table by using an algorithm for minimal DFAs to build a table for the reversal of the language in question and showing that we can derive the minimal RFSA from it after some simple ...
Anna Kasprzik +2 more
core +2 more sources
Terahertz image denoising via multiscale hybrid‐convolution residual network
CAAI Transactions on Intelligence TechnologyTerahertz imaging technology has great potential applications in areas, such as remote sensing, navigation, security checks, and so on. However, terahertz images usually have the problems of heavy noises and low resolution.
Heng Wu +4 more
doaj +1 more source
Sentinel-2 Sharpening via Parallel Residual Network
Remote Sensing, 2020 Sentinel-2 data is of great utility for a wide range of remote sensing applications due to its free access and fine spatial-temporal coverage. However, restricted by the hardware, only four bands of Sentinel-2 images are provided at 10 m resolution ...
Jiemin Wu, Zhi He, Jie Hu
doaj +1 more source
RESIDUAL LEARNING BASED IMAGE DENOISING AND COMPRESSION USING DNCNN
ICTACT Journal on Image and Video Processing, 2022 Image compression has become an essential subfield in image processing for many generations. This should be an effective process with decreasing this amount about a file format through frames unless significantly lowering from an exceptional standard ...
Savaram Shaliniswetha +1 more
doaj +1 more source
Deep Residual Learning for Small-Footprint Keyword Spotting
, 2018 We explore the application of deep residual learning and dilated convolutions to the keyword spotting task, using the recently-released Google Speech Commands Dataset as our benchmark.
Lin, Jimmy, Tang, Raphael
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Introduction We developed MedSupport, a multilevel medication adherence intervention designed to address root barriers to medication adherence. This study sought to explore the feasibility and acceptability of the MedSupport intervention strategies to support a future full‐scale randomized controlled trial.
Elizabeth G. Bouchard +8 more
wiley +1 more source
Evaluating the impact of residual learning and feature fusion on soil moisture prediction accuracy
Applied Computer ScienceThe architectural design of deep learning models significantly influences their predictive capabilities in environmental monitoring tasks. This paper investigates the individual and collective effects of residual learning and feature fusion mechanism to
Pascal YAMAKILI +2 more
doaj +1 more source
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)While Transformer models have achieved remarkable success in various domains, the effectiveness of information propagation through deep networks remains a critical challenge. Standard hidden state residuals often fail to adequately preserve initial token-level information in deeper layers.
Zhou, Zhanchao +4 more
openaire +2 more sources