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Unsupervised Learning of Edges
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Is this form of strong, highlevel supervision actually necessary to learn to accurately detect edges? ...
Using a large corpus of video data, we show that edge detectors trained using our unsupervised scheme approach the performance of the same methods trained with full supervision (within 3-5%). ...
Yin Li gratefully acknowledges the support of the Intel ISTC-PC while completing the writing of the paper at Georgia Tech. ...
doi:10.1109/cvpr.2016.179
dblp:conf/cvpr/LiPRD16
fatcat:ltclg336tbey3p4va4wq2gwqma
Unsupervised Learning of Edges
[article]
2016
arXiv
pre-print
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Is this form of strong, high-level supervision actually necessary to learn to accurately detect edges? ...
Using a large corpus of video data, we show that edge detectors trained using our unsupervised scheme approach the performance of the same methods trained with full supervision (within 3-5%). ...
Yin Li gratefully acknowledges the support of the Intel ISTC-PC while completing the writing of the paper at Georgia Tech. ...
arXiv:1511.04166v2
fatcat:7aqvjj2gofgm5cjgb3yxxgd4y4
Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency
[article]
2017
arXiv
pre-print
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Both layers are computed with awareness of edges inside the image to help address the issue of depth/normal discontinuity and preserve sharp edges. ...
Specifically, we formulate an edge-aware depth-normal consistency term, and solve it by constructing a depth-to-normal layer and a normal-to-depth layer inside of the DCN. ...
Conclusion In this paper, we propose an unsupervised learning framework for both depth and normal estimation via edge-aware depthnormal consistency. ...
arXiv:1711.03665v1
fatcat:wqtdxxk3dzdslej5t7da324hya
Unsupervised Learning Optical Flow by Robust Reconstruction and Edge-Aware Smoothing
2019
Australian Journal of Intelligent Information Processing Systems
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In this work, we present a novel unsupervised method that can learn optical flow by robust image reconstruction based on brightness constancy and structure similarity constraints. ...
To overcome negative effects of occlusion and obtain natural dense pixel flow fields, we propose an edge-aware loss function for smoothing flow fields while following original image structure. ...
Conclusion In this paper, we propose an unsupervised learning framework of optical flow which is robust in real scenes. ...
dblp:journals/ajiips/KongY19
fatcat:jjevcywajbdajcpijchvims3hm
Unsupervised Learning of Geometry From Videos With Edge-Aware Depth-Normal Consistency
2018
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Both layers are computed with awareness of edges inside the image to help address the issue of depth/normal discontinuity and preserve sharp edges. ...
In this paper, we propose to use surface normal representation for unsupervised depth estimation framework. ...
Thus, we incorporate an edge-aware depth-normal consistency constraint inside the network which better regularizes the learning of depths (Sec. 4). ...
doi:10.1609/aaai.v32i1.12257
fatcat:23eodozk7zhxpm3tcchqjqtk24
Unsupervised Learning of Discriminative Edge Measures for Vehicle Matching between Nonoverlapping Cameras
2008
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The weight of each match measure in the final decision is determined by a novel unsupervised learning process so that the same-different classification can be optimally separated in the combined measurement ...
We employ a novel measurement vector consists of three independent edge-based measures and their associated robust measures computed from a pair of aligned vehicle edge maps. ...
Any representations of civilians or civilian vehicles are an artifact of the experimental process and are used purely to expedite the research. ...
doi:10.1109/tpami.2007.70728
pmid:18276974
fatcat:nxwq5cvzynachhqtzgjsq7f3ha
Minority Resampling Boosted Unsupervised Learning with Hyperdimensional Computing for Threat Detection at the Edge of Internet of Things
2021
IEEE Access
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Next, the adaptation of the GSOM algorithm based on HD computing performs unsupervised learning from unlabeled data, within the bounds of the computational constraints of the Edge layer. ...
More specifically, unsupervised machine learning methods are technically suited for the detection of behaviour-based cyber threat and attacks on IoT Edge as it can learn from unlabeled data [20] . ...
She is presently the Director of the Centre for Open and Distance Learning at the University of Moratuwa. ...
doi:10.1109/access.2021.3111053
fatcat:ewqph2gvtfhijfqaafhnbmqk5m
Unsupervised Learning of Discriminative Edge Measures for Vehicle Matching between Non-Overlapping Cameras
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
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The weight of each match measure in the final decision is determined by a novel unsupervised learning process so that the same-different classification can be optimally separated in the combined measurement ...
We employ a novel measurement vector consists of three independent edge-based measures and their associated robust measures computed from a pair of aligned vehicle edge maps. ...
Any representations of civilians or civilian vehicles are an artifact of the experimental process and are used purely to expedite the research. ...
doi:10.1109/cvpr.2005.358
dblp:conf/cvpr/ShanSK05
fatcat:ilteybaepvcntdtiyaneisifvq
R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning
[article]
2020
arXiv
pre-print
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Our method is notably the first graph-based model that attempts to make use of deep learning representations for the task of unsupervised prerequisite learning. ...
The task of concept prerequisite chain learning is to automatically determine the existence of prerequisite relationships among concept pairs. ...
Notably, it is the first graph-based model that attempts to make use of deep learning representations for the task of unsupervised prerequisite learning. ...
arXiv:2004.10610v1
fatcat:vefwnxirpfgnnldmwvxjoffcna
Unsupervised Training for Neural TSP Solver
[article]
2022
arXiv
pre-print
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To avoid these problems, we introduce a novel unsupervised learning approach. ...
Our approach has the advantage over supervised learning of not requiring large labelled datasets. ...
Unsupervised learning has the advantage over supervised learning of not needing large correctly labeled datasets. ...
arXiv:2207.13667v1
fatcat:gvk4orinlffrfmym2lfj3cwpzm
Improved Extraction of Objects from Urine Microscopy Images with Unsupervised Thresholding and Supervised U-net Techniques
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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To the best of our knowledge this is the first application of Deep Learning for extraction of clinically significant objects in urine microscopy images. ...
Overall the proposed unsupervised method along with edge thresholding worked the best by extracting maximum number of objects and minimum number of artifacts. ...
The higher performance of the proposed unsupervised method as compared to the Deep Learning-based U-net method is possibly due to lack of sufficiently large data for training the Deep Learning method. ...
doi:10.1109/cvprw.2018.00299
dblp:conf/cvpr/AzizPCD18
fatcat:4mwly43knnhr5al4pprfq2x5c4
PTE
2015
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15
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One possible reason is that these text embedding methods learn the representation of text in a fully unsupervised way, without leveraging the labeled information available for the task. ...
Predictive text embedding utilizes both labeled and unlabeled data to learn the embedding of text. ...
Because of the unsupervised learning process, the representations learned through these text embedding models are general enough and can be applied to a variety of tasks such as classification, clustering ...
doi:10.1145/2783258.2783307
dblp:conf/kdd/TangQM15
fatcat:lmjjjaflz5cxflatwplflftvtq
Study on a New Method of Link-Based Link Prediction in the Context of Big Data
2021
Applied Bionics and Biomechanics
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A barrage of literature has been written to approach this problem; however, they mostly come from the angle of unsupervised learning (UL). ...
This research is aimed at finding the most appropriate link-based link prediction methods in the context of big data based on supervised learning. ...
Conflicts of Interest The authors declare no conflict of interest in the authorship of this article. ...
doi:10.1155/2021/1654134
fatcat:lzra62guyfdf7nkxkcfbqvujbi
Unsupervised Learning of Discriminative Attributes and Visual Representations
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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While most attribute learning methods are supervised by costly human-generated labels, we introduce a simple yet powerful unsupervised approach to learn and predict visual attributes directly from data ...
The learned attributes are shown to be capable of encoding rich imagery properties from both natural images and contour patches. ...
We employ a target of K +1 labels, all normalized to {0, 1} and consisting of one binary edge label and the K-bit attributes previously learned from the corresponding binary edge patch. ...
doi:10.1109/cvpr.2016.559
dblp:conf/cvpr/HuangLT16
fatcat:zt4qarlwobbslb52b5ylylelvu
Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus
[article]
2020
arXiv
pre-print
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The unsupervised learning process is repeated over several generations, in which each edge becomes a "student" and also part of different ensemble "teachers" for training other students. ...
We address the challenging problem of semi-supervised learning in the context of multiple visual interpretations of the world by finding consensus in a graph of neural networks. ...
Unsupervised learning over multiple iterations: The unsupervised learning experiments follow the steps of Algorithm 1: during the supervised stage (Step 1), we use the 8k labeled images (train set) to ...
arXiv:2010.01086v2
fatcat:ng27p5utdnabplogh5qhokdlh4
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