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
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network
[article]
2015
arXiv
pre-print
Preserved Fulltext
We propose a novel weakly-supervised semantic segmentation algorithm based on Deep Convolutional Neural Network (DCNN). ...
To make the segmentation knowledge transferrable across categories, we design a decoupled encoder-decoder architecture with attention model. ...
Recent breakthrough in semantic segmentation has been mainly accelerated by the approaches based on Convolutional Neural Networks (CNNs) [21, 4, 11, 10, 25] . ...
arXiv:1512.07928v1
fatcat:p6kdgj7gbvdrrgxzrdhss275k4
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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 2016; you can also visit the original URL.
The file type is application/pdf.
We propose a novel weakly-supervised semantic segmentation algorithm based on Deep Convolutional Neural Network (DCNN). ...
To make segmentation knowledge transferrable across categories, we design a decoupled encoder-decoder architecture with attention model. ...
Recent breakthrough in semantic segmentation has been mainly accelerated by the approaches based on Convolutional Neural Networks (CNNs) [4, 21, 11, 10, 25] . ...
doi:10.1109/cvpr.2016.349
dblp:conf/cvpr/HongOLH16
fatcat:obemojsyvvfflodeaxd3ptfyse
Transfer Learning from Audio-Visual Grounding to Speech Recognition
2019
Interspeech 2019
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.
Transfer learning aims to reduce the amount of data required to excel at a new task by re-using the knowledge acquired from learning other related tasks. ...
As semantics of speech are largely determined by its lexical content, grounding models learn to preserve phonetic information while disregarding uncorrelated factors, such as speaker and channel. ...
Deep audiovisual embedding network (DAVEnet) [21] is a two-branched convolutional neural network model for this task, which learns to encode images and spoken captions into a shared embedding space that ...
doi:10.21437/interspeech.2019-1227
dblp:conf/interspeech/HsuHG19
fatcat:vtf6iei6xfh5rela5wa77r6brq
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search
[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
These challenges preclude their applicability, and motivate our proposal of CATCH, a novel Context-bAsed meTa reinforcement learning (RL) algorithm for transferrable arChitecture searcH. ...
The contexts also assist a network evaluator in filtering inferior candidates and speed up learning. ...
Introduction The emergence of many high-performance neural networks has been one of the pivotal forces pushing forward the progress of deep learning research and production. ...
arXiv:2007.09380v3
fatcat:3mqj2rjlgvaprpxr6knxlx5x44
Towards Single Stage Weakly Supervised Semantic Segmentation
[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.
Other Versions
The costly process of obtaining semantic segmentation labels has driven research towards weakly supervised semantic segmentation (WSSS) methods, using only image-level, point, or box labels. ...
The multi-stage approach is computationally expensive, and dependency on image-level labels for CAMs generation lacks generalizability to more complex scenes. ...
Learning transferrable knowledge for semantic seg- Hsuan Yang. Weakly supervised object localization with
mentation with deep convolutional neural network. ...
arXiv:2106.10309v2
fatcat:l3oafc7rz5frbiynru2vn6ogfa
Snapshot Distillation: Teacher-Student Optimization in One Generation
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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.
Optimizing a deep neural network is a fundamental task in computer vision, yet direct training methods often suffer from over-fitting. ...
We also verify that models pre-trained with SD transfers well to object detection and semantic segmentation in the PascalVOC dataset. ...
Introduction A large portion of recent advances in computer vision have been built upon deep learning, in particular training very deep neural networks. ...
doi:10.1109/cvpr.2019.00297
dblp:conf/cvpr/YangXSY19
fatcat:sbd6ifmqhbe4hfoeekllcrnprm
Snapshot Distillation: Teacher-Student Optimization in One Generation
[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.
Optimizing a deep neural network is a fundamental task in computer vision, yet direct training methods often suffer from over-fitting. ...
We also verify that models pre-trained with SD transfers well to object detection and semantic segmentation in the PascalVOC dataset. ...
Introduction A large portion of recent advances in computer vision have been built upon deep learning, in particular training very deep neural networks. ...
arXiv:1812.00123v1
fatcat:o7fpnqeulffshgsbgvpjvydcfi
Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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.
Although the geometry and highlevel semantics are seemingly distant topics, surprisingly, we find that the convolutional neural networks pre-trained by the geometry cues can be effectively adapted to semantic ...
noisy and indirect training signals for learning the video representations. ...
To recognize actions and events happening in videos, recent approaches that employ deep convolutional neural networks (CNNs) [12, 17, 31, 34, 35] , recurrent networks [15, 33, 4] , and attention networks ...
doi:10.1109/cvpr.2018.00586
dblp:conf/cvpr/GanGLSG18
fatcat:fris6ki3zbasdd7t36jomx24hy
Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students
[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 focus on the problem of training a deep neural network in generations. ...
Consequently, the teacher provides a milder supervision signal (a less peaked distribution), and makes it possible for the student to learn from inter-class similarity and potentially lower the risk of ...
This research votes for the viewpoint that network optimization is far from perfect at the current status. ...
arXiv:1805.05551v2
fatcat:3e2yziuwurbhtfdfn25jzzhpjq
Concept Representation Learning with Contrastive Self-Supervised Learning
[article]
2022
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 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Concept-oriented deep learning (CODL) is a general approach to meet the future challenges for deep learning: (1) learning with little or no external supervision, (2) coping with test examples that come ...
from a different distribution than the training examples, and (3) integrating deep learning with symbolic AI. ...
Acknowledgement: Thanks to my wife Hedy (郑期芳) for her support. ...
arXiv:2112.05677v2
fatcat:z2yfbtrsvzd4ractfjrechopqa
Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation
[article]
2016
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 work we investigate the problem of road scene semantic segmentation using Deconvolutional Networks (DNs). ...
of knowledge from S-Net to the memory-efficient target network (T-Net). ...
With the arrival of deep convolutional neural networks (CNNs), hand-crafted features were substituted by learned CNN representations, which worked at the level of image patches [23, 24] . ...
arXiv:1604.01545v1
fatcat:flm7e6sd35av7c45ycvr7zx5nu
Attacking Point Cloud Segmentation with Color-only Perturbation
[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.
Other Versions
Recent research efforts on 3D point-cloud semantic segmentation have achieved outstanding performance by adopting deep CNN (convolutional neural networks) and GCN (graph convolutional networks). ...
Given that semantic segmentation has been applied in many safety-critical applications (e.g., autonomous driving, geological sensing), it is important to fill this knowledge gap, in particular, how these ...
Kpconv: Flexible and
Minimal adversarial examples for deep learning on 3d point deformable convolution for point clouds. ...
arXiv:2112.05871v2
fatcat:rru4yw6drjbrvpboxaruqo7asa
Deep Learning for Political Science
[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.
The discussion of deep neural networks is illustrated with the NLP tasks that are relevant to political science. ...
The latest advances in deep learning methods for NLP are also reviewed, together with their potential for improving information extraction and pattern recognition from political science texts. ...
transfer learning, we are also further understanding what we are learning with the deep neural networks. ...
arXiv:2005.06540v1
fatcat:kz2cbxjrmrfhdlfss5gqziefoq
Artificial Intelligence in Quantitative Ultrasound Imaging: A Review
[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.
Quantitative ultrasound (QUS) imaging is a reliable, fast and inexpensive technique to extract physically descriptive parameters for assessing pathologies. ...
Yap et al. used the fully convolutional networks (FCNs) for semantic segmentation of breast lesions on BUS images [141] . ...
Dezaki et al. developed a deep residual neural network (ResNet) combined with RNN for automatic recognition of cardiac cycle phase [78] . ...
arXiv:2003.11658v1
fatcat:iujuh7gra5ax7od2gxoo6yrbpe
Recent advances and clinical applications of deep learning in medical image analysis
[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:2105.13381v1 fatcat:yxa6asxvcra3bixp4habjdbcbaOther Versions
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging ...
scenarios, including classification, segmentation, detection, and image registration. ...
allowing for training very deep networks. ...
arXiv:2105.13381v2
fatcat:2k342a6rhjaavpoa2qoqxhg5rq
« Previous
Showing results 1 — 15 out of 145 results