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Found 5 papers, 2 papers with code
ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation
Monocular 3D human pose estimation (3D-HPE) is an inherently ambiguous task, as a 2D pose in an image might originate from different possible 3D poses.
DiffHPE: Robust, Coherent 3D Human Pose Lifting with Diffusion
We present an innovative approach to 3D Human Pose Estimation (3D-HPE) by integrating cutting-edge diffusion models, which have revolutionized diverse fields, but are relatively unexplored in 3D-HPE.
Data augmentation for learning predictive models on EEG: a systematic comparison
Our experiments also show that there is no single best augmentation strategy, as the good augmentations differ on each task.
Deep invariant networks with differentiable augmentation layers
Practitioners can typically enforce a desired invariance on the trained model through the choice of a network architecture, e. g. using convolutions for translations, or using data augmentation.
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals
Data augmentation is a key element of deep learning pipelines, as it informs the network during training about transformations of the input data that keep the label unchanged.
