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Found 3 papers, 1 papers with code
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders
Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially.
Ranked #4 on
Image Clustering
on Coil-20
A Fast and Compact Saliency Score Regression Network Based on Fully Convolutional Network
In this paper, we tackle this problem by proposing a fast and compact saliency score regression network which employs fully convolutional network, a special deep convolutional neural network, to estimate the saliency of objects in images.
Image Denoising via Multi-scale Nonlinear Diffusion Models
However, in recent two years, discriminatively trained local approaches have started to outperform previous non-local models and have been attracting increasing attentions due to the additional advantage of computational efficiency.
