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Algorithmic fairness in artificial intelligence for medicine and healthcare
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
A comprehensive survey on test-time adaptation under distribution shifts
Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …
process that can effectively generalize to test samples, even in the presence of distribution …
Test-time prompt tuning for zero-shot generalization in vision-language models
Pre-trained vision-language models (eg, CLIP) have shown promising zero-shot
generalization in many downstream tasks with properly designed text prompts. Instead of …
generalization in many downstream tasks with properly designed text prompts. Instead of …
Continual test-time domain adaptation
Test-time domain adaptation aims to adapt a source pre-trained model to a target domain
without using any source data. Existing works mainly consider the case where the target …
without using any source data. Existing works mainly consider the case where the target …
Efficient test-time model adaptation without forgetting
Test-time adaptation provides an effective means of tackling the potential distribution shift
between model training and inference, by dynamically updating the model at test time. This …
between model training and inference, by dynamically updating the model at test time. This …
Robust test-time adaptation in dynamic scenarios
Test-time adaptation (TTA) intends to adapt the pretrained model to test distributions with
only unlabeled test data streams. Most of the previous TTA methods have achieved great …
only unlabeled test data streams. Most of the previous TTA methods have achieved great …
Contrastive test-time adaptation
Test-time adaptation is a special setting of unsupervised domain adaptation where a trained
model on the source domain has to adapt to the target domain without accessing source …
model on the source domain has to adapt to the target domain without accessing source …
Source-free domain adaptive human pose estimation
Abstract Human Pose Estimation (HPE) is widely used in various fields, including motion
analysis, healthcare, and virtual reality. However, the great expenses of labeled real-world …
analysis, healthcare, and virtual reality. However, the great expenses of labeled real-world …
Knowledge distillation with the reused teacher classifier
Abstract Knowledge distillation aims to compress a powerful yet cumbersome teacher model
into a lightweight student model without much sacrifice of performance. For this purpose …
into a lightweight student model without much sacrifice of performance. For this purpose …
Guiding pseudo-labels with uncertainty estimation for source-free unsupervised domain adaptation
Abstract Standard Unsupervised Domain Adaptation (UDA) methods assume the availability
of both source and target data during the adaptation. In this work, we investigate Source-free …
of both source and target data during the adaptation. In this work, we investigate Source-free …