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Learning to Sample the Most Useful Training Patches from Images

Learning to Sample the Most Useful Training Patches from Images

24 November 2020
Shuyang Sun
Liang Chen
Greg Slabaugh
Juil Sock
ArXiv (abs)PDFHTML

Papers citing "Learning to Sample the Most Useful Training Patches from Images"

4 / 4 papers shown
ISP meets Deep Learning: A Survey on Deep Learning Methods for Image
  Signal Processing
ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal ProcessingACM Computing Surveys (ACM Comput. Surv.), 2023
Matheus Henrique Marques da Silva
Jhessica Victoria Santos da Silva
Rodrigo Reis Arrais
Wladimir Barroso Guedes de Araújo Neto
Leonardo Tadeu Lopes
...
Lucas B. Rondon
Bruno Melo de Souza
Mayara Costa Regazio
Rodolfo Coelho Dalapicola
C. F. G. Santos
SupRVLM
224
13
0
19 May 2023
SamplingAug: On the Importance of Patch Sampling Augmentation for Single
  Image Super-Resolution
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution
Shizun Wang
Ming Lu
Kaixin Chen
Jiaming Liu
Xiaoqi Li
Chuang Zhang
Ming Wu
200
9
0
30 Nov 2021
TransMix: Attend to Mix for Vision Transformers
TransMix: Attend to Mix for Vision Transformers
Jieneng Chen
Shuyang Sun
Ju He
Juil Sock
Alan Yuille
S. Bai
ViT
456
126
0
18 Nov 2021
Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive
  Learning from a Class-wise Memory Bank
Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory BankIEEE International Conference on Computer Vision (ICCV), 2021
Inigo Alonso
Alberto Sabater
David Ferstl
Luis Montesano
Ana C. Murillo
SSLCLL
593
259
0
27 Apr 2021
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