综述标题:Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions 论文链接:[2303.12484] Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions (arxiv.org) 摘要: 近年来,深度学习发展迅速,并在广泛的应用中取得了最先进的性能。然而,训练模...
Title: Masked Siamese Networks for Label-Efficient LearningFrom Facebook.ArXiv 2022.04.14 Highlight 本文提出了一种新的自监督pretrain的方法,Masked Siamese Networks(MSE)。实验发现这个pretrain在100% 的数据都有label的情况下的效果和MoCo-v3等一众自监督方法效果差不多,同时作者发现在极度semi-supervised的set...
In this paper, we investigate the use of Approximate Convex Decompositions (ACD) as a self-supervisory signal for label-efficient learning of point cloud representations. We show that using ACD to approximate ground truth segmentation provides excellent self-supervision for learning 3D point cloud ...
<br/>LabelEfficientLearningofTransferable<br/>RepresentationsacrossDomainsandTasks<br/>ZelunLuo<br/>StanfordUniversity<br/>zelunluo@stanford.edu<br/>YuliangZou<br/>Virginia..
Security Insights Additional navigation options main Branches 0Tags Code MSNMaskedSiameseNetworks This repo provides a PyTorch implementation of MSN (MaskedSiameseNetworks), as described in the paperMasked Siamese Networks for Label-Efficient Learning. ...
This paper presents the first comprehensive survey of label-efficient learning of point clouds. We address three critical questions in this emerging research field: i) the importance and urgency of label-efficient learning in point cloud processing, ii) the subfields it encompasses, and iii) the ...
Awesome Label-Efficient Learning in Agricutlture: A Comprehensive Review A curated list of awesome Label-efficient Learning in Agricutlture papers 🔥🔥🔥. Currently maintained by Jiajia Li @ MSU and Dong Chen @ MSU. Work still in progress 🚀, we appreciate any suggestions and contributions...
In computational pathology, multiple instance learning (MIL) is widely used to circumvent the computational impasse in giga-pixel whole slide image (WSI) analysis. It usually consists of two stages: patch-level feature extraction and slide-level aggregation. Recently, pretrained models or self-supervi...
3D point cloud; autoencoder; label-efficient; LoD3 building; unsupervised deep learning1. Introduction The diffusion of buildings’ point clouds at a high Level of Detail (LoD) [1] such as LoD3 provides very detailed geometrical representation and semantic information [2], which enables and ...
We discuss the problem of learning to rank labels from a real valued feedback associated with each label. We cast the feedback as a preferences graph where the nodes of the graph are the labels and edges express preferences over labels. We tackle the learning problem by defining a loss func...