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Found 6 papers, 4 papers with code
Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor Domains
We find that existing OOD detection methods do not cope with the covariate shifts in ImageNet-ES, implying that the definition and detection of OOD should be revisited to embrace real-world distribution shifts.
SlAction: Non-intrusive, Lightweight Obstructive Sleep Apnea Detection using Infrared Video
Recognizing that sleep videos exhibit minimal motion, this work investigates the fundamental question: "Are respiratory events adequately reflected in human motions during sleep?"
UpCycling: Semi-supervised 3D Object Detection without Sharing Raw-level Unlabeled Scenes
In this paper, we propose UpCycling, a novel SSL framework for 3D object detection with zero additional raw-level point cloud: learning from unlabeled de-identified intermediate features (i. e., smashed data) to preserve privacy.
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning Approach
By comparing their performance to (bitwidth-dedicated) QAT, existing bitwidth adaptive QAT and vanilla meta-learning, we find that merging bitwidths into meta-learning tasks achieves a higher level of robustness.
Federated Semi-Supervised Learning with Prototypical Networks
Compared to a FSSL approach based on weight sharing, the prototype-based inter-client knowledge sharing significantly reduces both communication and computation costs, enabling more frequent knowledge sharing between more clients for better accuracy.
Personalized Federated Learning with Server-Side Information
Personalized Federated Learning (FL) is an emerging research field in FL that learns an easily adaptable global model in the presence of data heterogeneity among clients.
