From f88030af4eb954b094578b35d8704376de73ec76 Mon Sep 17 00:00:00 2001 From: Lin Manxi <46046604+mmmmimic@users.noreply.github.com> Date: Sat, 10 Apr 2021 00:43:57 +0200 Subject: [PATCH] Update README.md --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index a27e051..fd5867e 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,9 @@ ## Introduction Recently, graph convolutional networks (GCNs) have been demonstrated efficient in learning graph representations. Regarding the down-sampling and up-sampling of non-Euclidean data, most existing methods are flat and lack robustness. We visualize the process of a state-of-the-art work DiffPool, and develop a novel differentiable module for upsampling called DiffUnpool. DiffPool and DiffUnpool learn soft cluster assignment for nodes via GCNs and multi-layer perceptrons respectively. To address the graph classification problem, based on DiffPool and DiffUnpool, we further propose an end-to-end encoder-decoder architecture, diff graph U-Net. Different from other U-shape models before, diff graph U-Net learns node embeddings hierarchically, and collect global features in residual fashion. Our experimental results show that our model yields an overall improvement of accuracy on 4 different data sets, compared with previous methods. +## Check our report +https://github.com/mmmmimic/02456-deep-learning-final-projektet/blob/master/report.pdf + ## Author Manxi Lin s192230 -- GitLab