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Found 3 papers, 2 papers with code
Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training
Notably, CONVMATCH achieves up to 95% of the prediction performance of GNNs on node classification while trained on graphs summarized down to 1% the size of the original graph.
Deep Perceptual Image Quality Assessment for Compression
In this paper we propose the largest image compression quality dataset to date with human perceptual preferences, enabling the use of deep learning, and we develop a full reference perceptual quality assessment metric for lossy image compression that outperforms the existing state-of-the-art methods.
Style is a Distribution of Features
We present a new algorithm for style transfer that fully extracts the style from the features by redefining the style loss as the Wasserstein distance between the distribution of features.
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