116 citations · 175 across the 24 of their papers we have counts for
8 papers · 1 filter
Label Shift Adapter for Test-Time Adaptation under Covariate and Label Shifts
Sunghyun Park, Seunghan Yang, Jaegul Choo +1
Test-time adaptation (TTA) aims to adapt a pre-trained model to the target domain in a batch-by-batch manner during inference. While label distributions often exhibit imbalances in…
Progressive Random Convolutions for Single Domain Generalization
Seokeon Choi, Debasmit Das, Sungha Choi +3
Single domain generalization aims to train a generalizable model with only one source domain to perform well on arbitrary unseen target domains. Image augmentation based on Random…
Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes
Sungha Choi, Seunghan Yang, Seokeon Choi +1
This paper proposes a novel test-time adaptation strategy that adjusts the model pre-trained on the source domain using only unlabeled online data from the target domain to allevia…
Distribution Estimation to Automate Transformation Policies for Self-Supervision
Seunghan Yang, Debasmit Das, Simyung Chang +2
In recent visual self-supervision works, an imitated classification objective, called pretext task, is established by assigning labels to transformed or augmented input images. The…
Arbitrary Style Transfer using Graph Instance Normalization
Dongki Jung, Seunghan Yang, Jaehoon Choi +1
Style transfer is the image synthesis task, which applies a style of one image to another while preserving the content. In statistical methods, the adaptive instance normalization…
Associative Partial Domain Adaptation
Youngeun Kim, Sungeun Hong, Seunghan Yang +3
Partial Adaptation (PDA) addresses a practical scenario in which the target domain contains only a subset of classes in the source domain. While PDA should take into account both c…
Partial Domain Adaptation Using Graph Convolutional Networks
Seunghan Yang, Youngeun Kim, Dongki Jung +1
Partial domain adaptation (PDA), in which we assume the target label space is included in the source label space, is a general version of standard domain adaptation. Since the targ…
Combinational Class Activation Maps for Weakly Supervised Object Localization
Seunghan Yang, Yoonhyung Kim, Youngeun Kim +1
Weakly supervised object localization has recently attracted attention since it aims to identify both class labels and locations of objects by using image-level labels. Most previo…