39 citations · 43 across the 5 of their papers we have counts for
3 papers · 1 filter
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels
Huaxi Huang, Hui Kang, Sheng Liu +4
Convolutional Neural Networks (CNNs) have demonstrated superiority in learning patterns, but are sensitive to label noises and may overfit noisy labels during training. The early s…
Learning Dense Correspondence from Synthetic Environments
Mithun Lal, Anthony Paproki, Nariman Habili +3
Estimation of human shape and pose from a single image is a challenging task. It is an even more difficult problem to map the identified human shape onto a 3D human model. Existing…
MongeNet: Efficient Sampler for Geometric Deep Learning
Léo Lebrat, Rodrigo Santa Cruz, Clinton Fookes +1
Recent advances in geometric deep-learning introduce complex computational challenges for evaluating the distance between meshes. From a mesh model, point clouds are necessary alon…