4 papers
Entropy Minimization without Model Collapse: Mitigating Prediction Bias in Medical Imaging
Tim Nielen, Sameer Ambekar, Johannes Kiechle +2
Entropy minimization (EM) is the dominant objective for test-time adaptation, yet its failure mode, model collapse, remains poorly understood. In this work, we show that distributi…
Hierarchical Adaptive networks with Task vectors for Test-Time Adaptation
Sameer Ambekar, Marta Hasny, Laura Daza +2
Test-time adaptation allows pretrained models to adjust to incoming data streams, addressing distribution shifts between source and target domains. However, standard methods rely o…
The Mean is the Mirage: Entropy-Adaptive Model Merging under Heterogeneous Domain Shifts in Medical Imaging
Sameer Ambekar, Reza Nasirigerdeh, Peter J. Schuffler +3
Model merging under unseen test-time distribution shifts often renders naive strategies, such as mean averaging unreliable. This challenge is especially acute in medical imaging, w…
GeneralizeFormer: Layer-Adaptive Model Generation across Test-Time Distribution Shifts
Sameer Ambekar, Zehao Xiao, Xiantong Zhen +1
We consider the problem of test-time domain generalization, where a model is trained on several source domains and adjusted on target domains never seen during training. Different…