1 citations · 1 across the 4 of their papers we have counts for
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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…
Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations
Sameer Ambekar, Julia A. Schnabel, Cosmin I. Bercea
Deep learning models in medical imaging often encounter challenges when adapting to new clinical settings unseen during training. Test-time adaptation offers a promising approach t…