168 citations · 340 across the 7 of their papers we have counts for
7 papers
Test-Time Training on Video Streams
Renhao Wang, Yu Sun, Arnuv Tandon +4
Prior work has established Test-Time Training (TTT) as a general framework to further improve a trained model at test time. Before making a prediction on each test instance, the mo…
Test-Time Training with Masked Autoencoders
Yossi Gandelsman, Yu Sun, Xinlei Chen +1
Test-time training adapts to a new test distribution on the fly by optimizing a model for each test input using self-supervision. In this paper, we use masked autoencoders for this…
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks
Binyan Hu, Yu Sun, A. K. Qin
Deep neural networks (DNNs) often rely on massive labelled data for training, which is inaccessible in many applications. Data augmentation (DA) tackles data scarcity by creating n…
Self-Supervised Policy Adaptation during Deployment
Nicklas Hansen, Rishabh Jangir, Yu Sun +5
In most real world scenarios, a policy trained by reinforcement learning in one environment needs to be deployed in another, potentially quite different environment. However, gener…
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
Mohammad Reza Loghmani, Luca Robbiano, Mirco Planamente +3
Unsupervised Domain Adaptation (DA) exploits the supervision of a label-rich source dataset to make predictions on an unlabeled target dataset by aligning the two data distribution…
Unsupervised Domain Adaptation through Self-Supervision
Yu Sun, Eric Tzeng, Trevor Darrell +1
This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target d…
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
Yu Sun, Xiaolong Wang, Zhuang Liu +3
In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions. W…