Results 291 to 300 of about 5,879,357 (336)

Unsupervised Learning of Robust Spectral Shape Matching

ACM Transactions on Graphics, 2023
We propose a novel learning-based approach for robust 3D shape matching. Our method builds upon deep functional maps and can be trained in a fully unsupervised manner.
Dongliang Cao, Paul Roetzer, Florian Bernard   +2 more
semanticscholar   +1 more source

Iterative Prompt Learning for Unsupervised Backlit Image Enhancement

IEEE International Conference on Computer Vision, 2023
We propose a novel unsupervised backlit image enhancement method, abbreviated as CLIP-LIT, by exploring the potential of Contrastive Language-Image Pre-Training (CLIP) for pixel-level image enhancement. We show that the openworld CLIP prior not only aids
Zhexin Liang, Chongyi Li, Shangchen Zhou, Ruicheng Feng, Chen Change Loy   +4 more
semanticscholar   +1 more source

Enhanced Autoencoders With Attention-Embedded Degradation Learning for Unsupervised Hyperspectral Image Super-Resolution

IEEE Transactions on Geoscience and Remote Sensing, 2023
Recently, unmixing-based networks have shown significant potential in unsupervised multispectral-aided hyperspectral image super-resolution (MS-aided HS-SR) task.
Lianru Gao, Jiaxin Li, Ke-xin Zheng, X. Jia   +3 more
semanticscholar   +1 more source

Unsupervised Learning

2022
Setareh Rafatirad, Houman Homayoun, Zhiqian Chen, Sai Manoj Pudukotai Dinakarrao   +3 more
  +8 more sources

Unsupervised Feature Learning via Non-parametric Instance Discrimination

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so.
Zhirong Wu, Yuanjun Xiong, Stella X. Yu, Dahua Lin   +3 more
semanticscholar   +1 more source

Deep Clustering for Unsupervised Learning of Visual Features

European Conference on Computer Vision, 2018
Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end-to-end training of visual features on large-scale datasets. In this work, we present
Mathilde Caron, Piotr Bojanowski, Armand Joulin, Matthijs Douze   +3 more
semanticscholar   +1 more source

Unsupervised Learning

Unsupervised learning enables identification of latent structure in unlabelled datasets when annotations are scarce or costly. We apply K-Means clustering and Principal Component Analysis (PCA) to a synthetic dataset of 10,000 observations with 15 features. K-Means (elbow criterion) produced four clusters (silhouette = 0.71). PCA reduced dimensionality
  +7 more sources

Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles

European Conference on Computer Vision, 2016
We propose a novel unsupervised learning approach to build features suitable for object detection and classification. The features are pre-trained on a large dataset without human annotation and later transferred via fine-tuning on a different, smaller ...
M. Noroozi, P. Favaro
semanticscholar   +1 more source

Unsupervised Learning: Clustering

2019
In this article an introduction on unsupervised cluster analysis is provided. Clustering is the organisation of unlabelled data into similarity groups called clusters. A cluster is a collection of data items which are similar between them, and dissimilar to data items in other clusters.
Serra A., Tagliaferri R.
openaire   +1 more source

Unsupervised learning

2020
Guillaume Coqueret, Tony Guida
  +5 more sources