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Found 5 papers, 3 papers with code
Boosting of Implicit Neural Representation-based Image Denoiser
Implicit Neural Representation (INR) has emerged as an effective method for unsupervised image denoising.
Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation
In contrast, the IID hypothesis is not universally guaranteed in numerous real-world applications, especially in medical image analysis.
Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation
In Stage \uppercase\expandafter{\romannumeral1}, we train the target model from scratch with soft pseudo-labels generated by the source model in a knowledge distillation manner.
Feature Alignment and Uniformity for Test Time Adaptation
Test time adaptation (TTA) aims to adapt deep neural networks when receiving out of distribution test domain samples.
Prototype Knowledge Distillation for Medical Segmentation with Missing Modality
Specifically, our ProtoKD can not only distillate the pixel-wise knowledge of multi-modality data to single-modality data but also transfer intra-class and inter-class feature variations, such that the student model could learn more robust feature representation from the teacher model and inference with only one single modality data.
