Yu Sun
EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2023-86
May 10, 2023
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-86.pdf
Most models in machine learning today are fixed during deployment. As a consequence, a trained model must prepare to be robust to all possible futures, even though only one of them is actually going to happen. The basic idea of test-time training is to train on this future once it arrives in the form of a test instance. Since each test instance arrives without a ground truth label, training is performed with self-supervision. This thesis explores the first steps in realizing this idea, for images, videos and robotics.
Advisor: Alexei (Alyosha) Efros and Moritz Hardt
BibTeX citation:
@phdthesis{Sun:EECS-2023-86, Author = {Sun, Yu}, Title = {Test-Time Training}, School = {EECS Department, University of California, Berkeley}, Year = {2023}, Month = {May}, URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-86.html}, Number = {UCB/EECS-2023-86}, Abstract = {Most models in machine learning today are fixed during deployment. As a consequence, a trained model must prepare to be robust to all possible futures, even though only one of them is actually going to happen. The basic idea of test-time training is to train on this future once it arrives in the form of a test instance. Since each test instance arrives without a ground truth label, training is performed with self-supervision. This thesis explores the first steps in realizing this idea, for images, videos and robotics.} }
EndNote citation:
%0 Thesis %A Sun, Yu %T Test-Time Training %I EECS Department, University of California, Berkeley %D 2023 %8 May 10 %@ UCB/EECS-2023-86 %U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-86.html %F Sun:EECS-2023-86