60 citations · 70 across the 5 of their papers we have counts for
6 papers
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters
Mateusz Michalkiewicz, Masoud Faraki, Xiang Yu +2
Overfitting to the source domain is a common issue in gradient-based training of deep neural networks. To compensate for the over-parameterized models, numerous regularization tech…
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction
Mateusz Michalkiewicz, Stavros Tsogkas, Sarah Parisot +3
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of…
A Simple and Scalable Shape Representation for 3D Reconstruction
Mateusz Michalkiewicz, Eugene Belilovsky, Mahsa Baktashmotlagh +1
Deep learning applied to the reconstruction of 3D shapes has seen growing interest. A popular approach to 3D reconstruction and generation in recent years has been the CNN encoder-…
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
Mateusz Michalkiewicz, Sarah Parisot, Stavros Tsogkas +3
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of…
Implicitly Defined Layers in Neural Networks
Qianggong Zhang, Yanyang Gu, Michalkiewicz Mateusz +2
In conventional formulations of multilayer feedforward neural networks, the individual layers are customarily defined by explicit functions. In this paper we demonstrate that defin…
Deep Level Sets: Implicit Surface Representations for 3D Shape Inference
Mateusz Michalkiewicz, Jhony K. Pontes, Dominic Jack +2
Existing 3D surface representation approaches are unable to accurately classify pixels and their orientation lying on the boundary of an object. Thus resulting in coarse representa…