Results 31 to 40 of about 6,131,436 (285)
Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning [PDF]
, 2019 We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only. This is achieved by extending recent ideas from learning of unsupervised image denoisers to unstructured 3D point clouds.
Hermosilla, Pedro +2 more
core +2 more sources
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 +5 more
doaj +1 more source
Breast Image Classification Based on Multi-feature Joint Supervised Dictionary Learning [PDF]
Jisuanji gongcheng, 2018 Aiming at the problem that the unsupervised dictionary learning algorithm has low image classification accuracy,a supervised dictionary learning classification algorithm which combines with multiple image features is proposed.It uses the convolution ...
LIU Lihui,XU Jun,GONG Lei
doaj +1 more source
Memristor Neural Network Training with Clock Synchronous Neuromorphic System
Micromachines, 2019 Memristor devices are considered to have the potential to implement unsupervised learning, especially spike timing-dependent plasticity (STDP), in the field of neuromorphic hardware research.
Sumin Jo +5 more
doaj +1 more source
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 We present a novel approach for unsupervised learning of depth and ego-motion from monocular video. Unsupervised learning removes the need for separate supervisory signals (depth or ego-motion ground truth, or multi-view video).
R. Mahjourian, M. Wicke, A. Angelova
semanticscholar +1 more source
Unsupervised online multitask learning of behavioral sentence embeddings [PDF]
PeerJ Computer Science, 2019 Appropriate embedding transformation of sentences can aid in downstream tasks such as NLP and emotion and behavior analysis. Such efforts evolved from word vectors which were trained in an unsupervised manner using large-scale corpora.
Shao-Yen Tseng +2 more
doaj +2 more sources
Machine Learning for Neuroimaging with Scikit-Learn [PDF]
, 2013 Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series.
Abraham, Alexandre +8 more
core +4 more sources
DualGAN: Unsupervised Dual Learning for Image-to-Image Translation [PDF]
IEEE International Conference on Computer Vision, 2017 Conditional Generative Adversarial Networks (GANs) for cross-domain image-to-image translation have made much progress recently [7, 8, 21, 12, 4, 18]. Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a ...
Zili Yi, Hao Zhang, P. Tan, Minglun Gong
semanticscholar +1 more source
IEEE Access, 2022 Unsupervised learning of feature representations is a challenging yet important problem for analyzing a large collection of multimedia data that do not have semantic labels.
Takahiko Furuya, Ryutarou Ohbuchi
doaj +1 more source
TUMK-ELM: A Fast Unsupervised Heterogeneous Data Learning Approach
IEEE Access, 2018 Advanced unsupervised learning techniques are an emerging challenge in the big data era due to the increasing requirements of extracting knowledge from a large amount of unlabeled heterogeneous data.
Lingyun Xiang +4 more
doaj +1 more source