Results 51 to 60 of about 6,131,436 (285)
Sensors, 2022 This paper proposes a novel unsupervised learning framework for depth recovery and camera ego-motion estimation from monocular video. The framework exploits the optical flow (OF) property to jointly train the depth and the ego-motion models.
Baigan Zhao +3 more
doaj +1 more source
Learning Features by Watching Objects Move
, 2017 This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation.
Darrell, Trevor +4 more
core +1 more source
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019 We propose a novel generative adversarial network (GAN) for the task of unsupervised learning of 3D representations from natural images. Most generative models rely on 2D kernels to generate images and make few assumptions about the 3D world.
Thu Nguyen-Phuoc +4 more
semanticscholar +1 more source
An Unsupervised Learning Model for Deformable Medical Image Registration [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 We present a fast learning-based algorithm for deformable, pairwise 3D medical image registration. Current registration methods optimize an objective function independently for each pair of images, which can be time-consuming for large data.
Guha Balakrishnan +4 more
semanticscholar +1 more source
Deep Temporal Iterative Clustering for Satellite Image Time Series Land Cover Analysis
Remote Sensing, 2022 The extensive amount of Satellite Image Time Series (SITS) data brings new opportunities and challenges for land cover analysis. Many supervised machine learning methods have been applied in SITS, but the labeled SITS samples are time- and effort ...
Wenqi Guo +4 more
doaj +1 more source
Local Aggregation for Unsupervised Learning of Visual Embeddings [PDF]
IEEE International Conference on Computer Vision, 2019 Unsupervised approaches to learning in neural networks are of substantial interest for furthering artificial intelligence, both because they would enable the training of networks without the need for large numbers of expensive annotations, and because ...
Chengxu Zhuang, Alex Zhai, Daniel Yamins
semanticscholar +1 more source
UniverseThis review investigates the application of unsupervised machine learning algorithms to astronomical data. Unsupervised machine learning enables researchers to analyze large, high-dimensional, and unlabeled datasets and is sometimes considered more ...
Chih-Ting Kuo, Duo Xu, Rachel Friesen
doaj +1 more source
Unsupervised Learning of Edges
, 2016 Data-driven approaches for edge detection have proven effective and achieve top results on modern benchmarks. However, all current data-driven edge detectors require manual supervision for training in the form of hand-labeled region segments or object ...
Dollár, Piotr +3 more
core +1 more source
Prefix Data Augmentation for Contrastive Learning of Unsupervised Sentence Embedding
Applied SciencesThis paper presents prefix data augmentation (Prd) as an innovative method for enhancing sentence embedding learning through unsupervised contrastive learning.
Chunchun Wang, Shu Lv
doaj +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Glioma recurrence severely impacts patient prognosis, with current treatments showing limited efficacy. Traditional methods struggle to analyze recurrence mechanisms due to challenges in assessing tumor heterogeneity, spatial dynamics, and gene networks.
Lei Qiu +10 more
wiley +1 more source