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Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from
  Mixture-of-Experts

Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts

8 October 2022
Tao Zhong
Zhixiang Chi
Li Gu
Yang Wang
Yuanhao Yu
Jingshan Tang
    OOD
ArXivPDFHTML

Papers citing "Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts"

5 / 5 papers shown
Title
Gradient-based Bi-level Optimization for Deep Learning: A Survey
Gradient-based Bi-level Optimization for Deep Learning: A Survey
Can Chen
Xiangshan Chen
Chen-li Ma
Zixuan Liu
Xue Liu
53
29
0
24 Jul 2022
Domain Impression: A Source Data Free Domain Adaptation Method
Domain Impression: A Source Data Free Domain Adaptation Method
V. Kurmi
Venkatesh Subramanian
Vinay P. Namboodiri
TTA
123
145
0
17 Feb 2021
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
Yinbo Chen
Zhuang Liu
Huijuan Xu
Trevor Darrell
Xiaolong Wang
137
269
0
09 Mar 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
220
9,525
0
09 Mar 2017
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
L. V. D. van der Maaten
Kilian Q. Weinberger
PINN
3DV
222
9,849
0
25 Aug 2016
1