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Found 8 papers, 3 papers with code
Enabling Uncertainty Estimation in Iterative Neural Networks
Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance.
PartAL: Efficient Partial Active Learning in Multi-Task Visual Settings
Active Learning (AL) can be used to reduce this burden.
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.
How to Boost Face Recognition with StyleGAN?
We show that a simple approach based on fine-tuning pSp encoder for StyleGAN allows us to improve upon the state-of-the-art facial recognition and performs better compared to training on synthetic face identities.
DEBOSH: Deep Bayesian Shape Optimization
Graph Neural Networks (GNNs) can predict the performance of an industrial design quickly and accurately and be used to optimize its shape effectively.
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
