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A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts

A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts

27 March 2023
Jian Liang
R. He
Tien-Ping Tan
    OOD
    VLM
    TTA
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Papers citing "A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts"

10 / 60 papers shown
Title
Visualizing Adapted Knowledge in Domain Transfer
Visualizing Adapted Knowledge in Domain Transfer
Yunzhong Hou
Liang Zheng
108
52
0
20 Apr 2021
The Power of Scale for Parameter-Efficient Prompt Tuning
The Power of Scale for Parameter-Efficient Prompt Tuning
Brian Lester
Rami Al-Rfou
Noah Constant
VPVLM
275
3,784
0
18 Apr 2021
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
130
150
0
17 Feb 2021
Unsupervised Domain Adaptation of Black-Box Source Models
Unsupervised Domain Adaptation of Black-Box Source Models
Haojian Zhang
Yabin Zhang
K. Jia
Lei Zhang
117
50
0
08 Jan 2021
Domain Adaptation for the Segmentation of Confidential Medical Images
Domain Adaptation for the Segmentation of Confidential Medical Images
Serban Stan
Mohammad Rostami
OOD
22
13
0
02 Jan 2021
Source Data-absent Unsupervised Domain Adaptation through Hypothesis
  Transfer and Labeling Transfer
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
Jian Liang
Dapeng Hu
Yunbo Wang
R. He
Jiashi Feng
128
249
0
14 Dec 2020
Casting a BAIT for Offline and Online Source-free Domain Adaptation
Casting a BAIT for Offline and Online Source-free Domain Adaptation
Shiqi Yang
Yaxing Wang
Joost van de Weijer
Luis Herranz
Shangling Jui
TTA
77
45
0
23 Oct 2020
A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation
A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation
Jian Liang
Yunbo Wang
Dapeng Hu
R. He
Jiashi Feng
129
104
0
05 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
234
11,568
0
09 Mar 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
243
9,042
0
06 Jun 2015
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