A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks
2017
Neural Information Processing Systems
Preserved Fulltext
In this paper, we propose to exploit unlabeled videos for semi-supervised learning of optical flow with a Generative Adversarial Network. ...
Convolutional neural networks (CNNs) have recently been applied to the optical flow estimation problem. ...
Semi-Supervised Optical Flow Estimation In this section, we describe the semi-supervised learning approach for optical flow estimation, the design methodology of the proposed generative adversarial network ...
dblp:conf/nips/LaiH017
fatcat:24mjzmgfvbfj5liplf7ikzyktu
SDOF-GAN: Symmetric Dense Optical Flow Estimation With Generative Adversarial Networks
2021
IEEE Transactions on Image Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We bridge this gap by introducing a novel model named SDOF-GAN: symmetric dense optical flow with generative adversarial networks (GANs). ...
In addition, SDOF-GAN leverages a GAN model for which the generator estimates symmetric optical flow fields while the discriminator differentiates the "real" ground-truth flow field from a "fake" estimation ...
Therefore, SDOF-GAN aims to minimize the L sup for labeled images with the ground-truth flow.
3) Adversarial Loss: To train the generator G and discriminator D with semi-supervised learning on both labeled ...
doi:10.1109/tip.2021.3084073
fatcat:3vsqjzcqyff6zjzkqfkbkgjqt4
Spoof Face Detection Via Semi-Supervised Adversarial Training
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a semi-supervised adversarial learning framework for spoof face detection, which largely relaxes the supervision condition. ...
., temporal information), we intuitively take the optical flow maps converted from consecutive video frames as input. ...
Motivated by the above limitations and analysis, we propose a novel adversarial network for anti-spoofing under the semi-supervised setting. ...
arXiv:2005.10999v1
fatcat:akn3bbfdgrczzdb476ym2ohiyy
A SEMI-SUPERVISED APPROACH TO SAR-OPTICAL IMAGE MATCHING
2019
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In doing so we make an initial contribution towards the use of semi-supervised learning for matching SAR and optical imagery. ...
In this paper we frame the matching problem within semi-supervised learning, and use this as a proxy for investigating the effects of data scarcity on matching. ...
of semi-supervised learning for SAR-optical matching. ...
doi:10.5194/isprs-annals-iv-2-w7-71-2019
fatcat:f5mqcmwp2nbfxmwdkuonif5xwy
Dual Discriminator Generative Adversarial Network for Video Anomaly Detection
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a semi-supervised approach with a dual discriminator-based generative adversarial network structure. ...
Fake optical flows are estimated from generated frames and adjacent frames, and real optical flows are estimated from the real frames sampled from original videos. ...
the stateof-the-art performance for semi-supervised video anomaly detection. ...
doi:10.1109/access.2020.2993373
fatcat:y7qrrnapcnbp3agngpwkr5csp4
Retinal Image Synthesis and Semi-supervised Learning for Glaucoma Assessment
2019
IEEE Transactions on Medical Imaging
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Recent works show that generative adversarial networks (GANs) can be successfully applied to image synthesis and semi-supervised learning, where, given a small labeled database and a large unlabeled database ...
In this paper, we trained a retinal image synthesizer and a semi-supervised learning method for automatic glaucoma assessment using an adversarial model on a small glaucoma-labeled database and a large ...
ACKNOWLEDGMENT We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. ...
doi:10.1109/tmi.2019.2903434
pmid:30843823
fatcat:2rq7d3uag5gzdjc6uecacdvqya
Adversarial Framework for Unsupervised Learning of Motion Dynamics in Videos
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Self-supervision is enforced by using motion masks produced by the generator, as a co-product of its generation process, to supervise the discriminator network in performing dense prediction. ...
In this paper, we propose an adversarial GAN-based framework that learns video representations and dynamics through a self-supervision mechanism in order to perform dense and global prediction in videos ...
the optical flow dense prediction stream and using masks synthesized by the generator for supervision. ...
arXiv:1803.09092v2
fatcat:tconl7knq5af3nqlbxthvx7br4
A Conditional Adversarial Network for Scene Flow Estimation
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The proposed network is the first attempt to estimate scene flow using generative adversarial networks, and is able to estimate both the optical flow and disparity from the input stereo images simultaneously ...
We propose a conditional adversarial network SceneFlowGAN for scene flow estimation. The proposed SceneFlowGAN uses loss function at two ends: both generator and descriptor ends. ...
There are a few learning based methods for scene flow estimation. This can be useful in semi-supervised learning scenario as well, since acquiring data will remain a challenge. ...
arXiv:1904.11163v1
fatcat:ztnbw3quxzcohoiqvx7dctml5i
Monocular Depth Estimation Based On Deep Learning: An Overview
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Furthermore, we review some representative existing methods according to different training manners: supervised, unsupervised and semi-supervised. ...
With the rapid development of deep neural networks, monocular depth estimation based on deep learning has been widely studied recently and achieved promising performance in accuracy. ...
Similarly, considering that optical flow is successfully solved by CNN through supervised learning, Mayer et al. [33] extend the optical flow networks to disparity and scene flow estimation. ...
arXiv:2003.06620v1
fatcat:l5ei3ognova6xkyppflef5nqsq
A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Quality Metric 596 Fully Automated Blind Color Deconvolution of Histopathological Images 599 A Pixel-wise Distance Regression Approach for Joint Retinal Optical Disc and Fovea Detection 606 Semi-supervised ...
for Fast Probabilistic Diffeomorphic Registration 286 Conditional Entropy as a Supervised Primitive Segmentation Loss Function 290 Adversarial Similarity Network for Evaluating Image Alignment in Deep ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
Spatiotemporal CNN for Video Object Segmentation
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
The spatial segmentation branch focuses on segmenting objects accurately based on the learned appearance and motion cues. ...
In this paper, we present a unified, end-to-end trainable spatiotemporal CNN model for VOS, which consists of two branches, i.e., the temporal coherence branch and the spatial segmentation branch. ...
Notably, in contrast to [25] , we do not generate the optical flow since our STCNN do not require the optical flow for video segmentation. ...
arXiv:1904.02363v1
fatcat:mmzxevx4hrcdzfnllksl6pyrjy
Adversarial Learning for Semi-Supervised Semantic Segmentation
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We propose a method for semi-supervised semantic segmentation using an adversarial network. ...
In addition, the fully convolutional discriminator enables semi-supervised learning through discovering the trustworthy regions in predicted results of unlabeled images, thereby providing additional supervisory ...
[40] propose to generate adversarial examples using GAN for semi-supervised semantic segmentation. ...
arXiv:1802.07934v2
fatcat:vzcurbdrgzdfflikr6sdjup6um
DiscrimNet: Semi-Supervised Action Recognition from Videos using Generative Adversarial Networks
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We determine good network architectural and hyperparameter settings for us- ing the discriminator from DCGAN as a trained model to learn useful representations for action recognition. ...
Our model involves train- ing a deep convolutional generative adversarial network (DCGAN) using a large video activity dataset without la- bel information. ...
Generative Adversarial Networks [4] have been used for semi-supervised feature learning particularly after the introduction of Deep Convolutional GANs (or DCGANs) [38] . ...
arXiv:1801.07230v1
fatcat:jadecr4slffrtezim3lrr2klva
Generation and Simulation of Yeast Microscopy Imagery with Deep Learning
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print arXiv:2103.11834v3 fatcat:jfl6vg62ybbstoey7svs5r2s3uOther Versions
To approach this problem, a novel generative adversarial network, for conditionalized and unconditionalized image generation, is proposed. ...
The obtained results showed that the modeling of TLFM experiments, with deep learning, is a proper approach, but requires future research to effectively model real-world experiments. ...
.5 Semi Supervised Learning
. 6 Semantic Segmentation
.7 Optical Flow
.1 Improved Generative Adversarial Network Methods
.1 Improved Generative Adversarial Network Methods
.1 ...
arXiv:2103.11834v4
fatcat:x6jljdhd4vcx7a3wjxj6igs57q
Deep learning approaches for real-time image super-resolution
2020
Neural computing & applications (Print)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The paper entitled "Perceptual image quality using dual generative adversarial network" develops a variety of generative adversarial networks for image SR that contains two generators and two discriminators ...
Recently, due to remarkable advances in deep learning, deep neural networks for SR have shown promising performance in several applications. ...
The paper ''A novel super-resolution CT image reconstruction via semi-supervised generative adversarial network'' proposes a novel semi-supervised generative adversarial network to accurately recover HR ...
doi:10.1007/s00521-020-05176-z
fatcat:gvmbzve6kvfmzfwumblipqzaji
« Previous
Showing results 1 — 15 out of 1,992 results