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Unsupervised Learning of Edges [PDF]

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
Camera ready version for CVPR ...
Yin Li 0003, Manohar Paluri, James M. Rehg, Piotr Dollár   +3 more
openaire   +3 more sources

UNSUPERVISED TRANSUDATIVE TL FEATURE LEARNING FOR IMAGE FEATURE EXTRACTION AND REPRESENTATION [PDF]

ICTACT Journal on Image and Video Processing, 2023
In this study, we address the problem of unsupervised transductive transfer learning for image feature extraction and representation. While transfer learning has shown promising results in various domains, its application to image feature extraction in ...
Logeshwari Dhavamani, A. Rajavel, Subhadra Mishra, Komal B Umare   +3 more
doaj   +1 more source

Unsupervised Learning of Monocular Depth Estimation:A Survey [PDF]

Jisuanji kexue
As the key point of 3D reconstruction,automatic driving and visual SLAM,depth estimation has always been a hot research direction in the field of computer vision,among which,monocular depth estimation technology based on unsupervised learning has been ...
CAI Jiacheng, DONG Fangmin, SUN Shuifa, TANG Yongheng
doaj   +1 more source

Stable and Fast Deep Mutual Information Maximization Based on Wasserstein Distance

Entropy, 2023
Deep learning is one of the most exciting and promising techniques in the field of artificial intelligence (AI), which drives AI applications to be more intelligent and comprehensive.
Xing He, Changgen Peng, Lin Wang, Weijie Tan, Zifan Wang   +4 more
doaj   +1 more source

DeConFuse: a deep convolutional transform-based unsupervised fusion framework

EURASIP Journal on Advances in Signal Processing, 2020
This work proposes an unsupervised fusion framework based on deep convolutional transform learning. The great learning ability of convolutional filters for data analysis is well acknowledged.
Pooja Gupta, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia   +4 more
doaj   +1 more source

ResNet Autoencoders for Unsupervised Feature Learning From High-Dimensional Data: Deep Models Resistant to Performance Degradation

IEEE Access, 2021
Efficient modeling of high-dimensional data requires extracting only relevant dimensions through feature learning. Unsupervised feature learning has gained tremendous attention due to its unbiased approach, no need for prior knowledge or expensive manual
Chathurika S. Wickramasinghe, Daniel L. Marino, Milos Manic   +2 more
doaj   +1 more source

Meta-Unsupervised-Learning: A supervised approach to unsupervised learning

CoRR, 2016
22 ...
Vikas K. Garg 0001, Adam Tauman Kalai
openaire   +3 more sources

Representational Bias in Unsupervised Learning of Syllable Structure [PDF]

, 2005
Unsupervised learning algorithms based on Expectation Maximization (EM) are often straightforward to implement and provably converge on a local likelihood maximum. However, these algorithms often do not perform well in practice.
Johnson, Mark, Sharon Goldwater, Mark Johnson, Goldwater, Sharon; id_orcid   +3 more
core   +1 more source

Learning from Multiple Instances: A Two-Stage Unsupervised Image Denoising Framework Based on Deep Image Prior

Applied Sciences, 2022
Supervised image denoising methods based on deep neural networks require a large amount of noisy-clean or noisy image pairs for network training. Thus, their performance drops drastically when the given noisy image is significantly different from the ...
Shaoping Xu, Xiaojun Chen, Yiling Tang, Shunliang Jiang, Xiaohui Cheng, Nan Xiao   +5 more
doaj   +1 more source

Unsupervised learning of overlapping image components using divisive input modulation [PDF]

, 2009
This paper demonstrates that nonnegative matrix factorisation is mathematically related to a class of neural networks that employ negative feedback as a mechanism of competition. This observation inspires a novel learning algorithm which we call Divisive
De Meyer, Kris, Spratling, Michael, Kompass, R., de Meyer, K., Spratling, M.W., Kompass, R   +5 more
core   +1 more source