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Found 7 papers, 2 papers with code
ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference
Sampling-free approaches can be faster but suffer from other drawbacks, such as lower reliability of uncertainty estimates, difficulty of use, and limited applicability to different types of tasks and data.
PartAL: Efficient Partial Active Learning in Multi-Task Visual Settings
Active Learning (AL) can be used to reduce this burden.
How to Boost Face Recognition with StyleGAN?
Our self-supervised strategy is the most useful with limited amounts of labeled training data, which can be beneficial for more tailored face recognition tasks and when facing privacy concerns.
DEBOSH: Deep Bayesian Shape Optimization
Shape optimization is at the heart of many industrial applications, such as aerodynamics, heat transfer, and structural analysis.
Leveraging Self-Supervision for Cross-Domain Crowd Counting
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density.
Masksembles for Uncertainty Estimation
Our central intuition is that there is a continuous spectrum of ensemble-like models of which MC-Dropout and Deep Ensembles are extreme examples.
Double Refinement Network for Efficient Indoor Monocular Depth Estimation
Monocular depth estimation is the task of obtaining a measure of distance for each pixel using a single image.
Indoor Monocular Depth Estimation
Monocular Depth Estimation
