Results 51 to 60 of about 9,759,453 (201)
Image data augmentation techniques based on deep learning: A survey
Mathematical Biosciences and EngineeringIn recent years, deep learning (DL) techniques have achieved remarkable success in various fields of computer vision. This progress was attributed to the vast amounts of data utilized to train these models, as they facilitated the learning of more ...
Wu Zeng
doaj +1 more source
SensorsSide-scan sonar is a principal technique for subsea target detection, where the quantity of sonar images of seabed targets significantly influences the accuracy of intelligent target recognition.
Chengyang Peng +3 more
doaj +1 more source
Discounting and Augmentation of Dispositional and Causal Attributions
Psychologica Belgica, 2006 This article investigates whether and how discounting and augmentation of dispositional and causal attributions differ between each other. In three experiments, the strength of a causal or dispositional attribution to a target actor (or object) was ...
Frank Van Overwalle
doaj +1 more source
Smart Augmentation - Learning an Optimal Data Augmentation Strategy [PDF]
, 2017 A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks(DNN).
Bazrafkan, Shabab +2 more
core +2 more sources
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingCollaborative representation (CR) models have been widely used in hyperspectral image (HSI) classification tasks. However, most CR classification models lack stability and generalization when targeting small samples as well as spatial homogeneity and ...
Hongjun Su +3 more
doaj +1 more source
MCMC for Imbalanced Categorical Data
, 2017 Many modern applications collect highly imbalanced categorical data, with some categories relatively rare. Bayesian hierarchical models combat data sparsity by borrowing information, while also quantifying uncertainty.
Dunson, David B. +3 more
core +2 more sources
Sample Efficiency of Data Augmentation Consistency Regularization
, 2022 Data augmentation is popular in the training of large neural networks; currently, however, there is no clear theoretical comparison between different algorithmic choices on how to use augmented data. In this paper, we take a step in this direction - we first present a simple and novel analysis for linear regression with label invariant augmentations ...
Yang, Shuo +5 more
openaire +2 more sources
, 2017 We present a convolutional neural network (CNN) based solution for modeling physically plausible spatially varying surface reflectance functions (SVBRDF) from a single photograph of a planar material sample under unknown natural illumination. Gathering a
Dong, Yue +3 more
core +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThe remote sensing mapping of paddy rice in Southwest China faces challenges such as fragmented parcels and difficulties in field sample collection, hindering deep learning technology applications. To address sample scarcity for deep learning-based paddy
Ziyi Tang +5 more
doaj +1 more source
Adjunctive quetiapine for serotonin reuptake inhibitor-resistant obsessive-compulsive disorder: A meta-analysis of randomised controlled treatment trials [PDF]
, 2006 Small studies have shown positive effects from adding a variety of antipsychotic agents in patients with obsessive–compulsive disorder who are unresponsive to treatment with serotonin reuptake inhibitors. The evidence, however, is contradictory.
Carey, P +8 more
core +1 more source