ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1911.09307
  4. Cited By
Patch-level Neighborhood Interpolation: A General and Effective
  Graph-based Regularization Strategy

Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy

21 November 2019
Ke Sun
Bin-Xia Yu
Zhouchen Lin
Zhanxing Zhu
ArXivPDFHTML

Papers citing "Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy"

4 / 4 papers shown
Title
MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuning
MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuning
Yangning Li
Zihua Lan
Lv Qingsong
Hai-Tao Zheng
Hai-Tao Zheng
31
0
0
09 Apr 2025
A Survey of Mix-based Data Augmentation: Taxonomy, Methods,
  Applications, and Explainability
A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability
Chengtai Cao
Fan Zhou
Yurou Dai
Jianping Wang
Kunpeng Zhang
AAML
24
28
0
21 Dec 2022
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
Jang-Hyun Kim
Wonho Choo
Hosan Jeong
Hyun Oh Song
197
176
0
05 Feb 2021
Improving neural networks by preventing co-adaptation of feature
  detectors
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
266
7,636
0
03 Jul 2012
1