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PI-Net: A Deep Learning Approach to Extract Topological Persistence
  Images

PI-Net: A Deep Learning Approach to Extract Topological Persistence Images

5 June 2019
Anirudh Som
Hongjun Choi
K. Ramamurthy
M. Buman
P. Turaga
    3DH
ArXivPDFHTML

Papers citing "PI-Net: A Deep Learning Approach to Extract Topological Persistence Images"

7 / 7 papers shown
Title
Leveraging Topological Guidance for Improved Knowledge Distillation
Leveraging Topological Guidance for Improved Knowledge Distillation
Eun Som Jeon
Rahul Khurana
Aishani Pathak
P. Turaga
49
0
0
07 Jul 2024
Topological Persistence Guided Knowledge Distillation for Wearable
  Sensor Data
Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data
Eun Som Jeon
Hongjun Choi
A. Shukla
Yuan Wang
Hyunglae Lee
M. Buman
P. Turaga
27
3
0
07 Jul 2024
Topological Attention for Time Series Forecasting
Topological Attention for Time Series Forecasting
Sebastian Zeng
Florian Graf
Christoph Hofer
Roland Kwitt
AI4TS
11
24
0
19 Jul 2021
Can neural networks learn persistent homology features?
Can neural networks learn persistent homology features?
Guido Montúfar
N. Otter
Yuguang Wang
23
12
0
30 Nov 2020
A Sheaf and Topology Approach to Generating Local Branch Numbers in
  Digital Images
A Sheaf and Topology Approach to Generating Local Branch Numbers in Digital Images
Chuan-Shen Hu
Yu-Min Chung
10
0
0
27 Nov 2020
Designing Deep Networks for Surface Normal Estimation
Designing Deep Networks for Surface Normal Estimation
X. Wang
David Fouhey
Abhinav Gupta
3DV
SSL
156
353
0
18 Nov 2014
Statistical topological data analysis using persistence landscapes
Statistical topological data analysis using persistence landscapes
Peter Bubenik
103
846
0
27 Jul 2012
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