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MOMA:Distill from Self-Supervised Teachers

MOMA:Distill from Self-Supervised Teachers

4 February 2023
Yuan Yao
Nandakishor Desai
M. Palaniswami
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Papers citing "MOMA:Distill from Self-Supervised Teachers"

7 / 7 papers shown
Title
A Unified View of Masked Image Modeling
A Unified View of Masked Image Modeling
Zhiliang Peng
Li Dong
Hangbo Bao
QiXiang Ye
Furu Wei
VLM
52
35
0
19 Oct 2022
Exploring Target Representations for Masked Autoencoders
Exploring Target Representations for Masked Autoencoders
Xingbin Liu
Jinghao Zhou
Tao Kong
Xianming Lin
Rongrong Ji
79
49
0
08 Sep 2022
MimCo: Masked Image Modeling Pre-training with Contrastive Teacher
MimCo: Masked Image Modeling Pre-training with Contrastive Teacher
Qiang-feng Zhou
Chaohui Yu
Haowen Luo
Zhibin Wang
Hao Li
VLM
54
20
0
07 Sep 2022
Masked Autoencoders Are Scalable Vision Learners
Masked Autoencoders Are Scalable Vision Learners
Kaiming He
Xinlei Chen
Saining Xie
Yanghao Li
Piotr Dollár
Ross B. Girshick
ViT
TPM
258
7,412
0
11 Nov 2021
Emerging Properties in Self-Supervised Vision Transformers
Emerging Properties in Self-Supervised Vision Transformers
Mathilde Caron
Hugo Touvron
Ishan Misra
Hervé Jégou
Julien Mairal
Piotr Bojanowski
Armand Joulin
292
5,761
0
29 Apr 2021
Improved Baselines with Momentum Contrastive Learning
Improved Baselines with Momentum Contrastive Learning
Xinlei Chen
Haoqi Fan
Ross B. Girshick
Kaiming He
SSL
238
3,359
0
09 Mar 2020
Semantic Understanding of Scenes through the ADE20K Dataset
Semantic Understanding of Scenes through the ADE20K Dataset
Bolei Zhou
Hang Zhao
Xavier Puig
Tete Xiao
Sanja Fidler
Adela Barriuso
Antonio Torralba
SSeg
249
1,821
0
18 Aug 2016
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