Deep Learning
Unsupervised Learning of Object Landmarks through Conditional Image Generation
Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi
Neural Information Processing Systems http://nips.cc/
Variational Denoising Network: Toward Blind Noise Modeling and Removal
Zongsheng Yue, Hongwei Yong, Qian Zhao, Deyu Meng, Lei Zhang
On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression. On the other hand, VDN inherits the advantages of traditional model-driven approaches, especially the good generalization capability of generative models.
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
Jiawang Bian, Zhichao Li, Naiyan Wang, Huangying Zhan, Chunhua Shen, Ming-Ming Cheng, Ian Reid
Recent work has shown that CNN-based depth and ego-motion estimators can be learned using unlabelled monocular videos. However, the performance is limited by unidentified moving objects that violate the underlying static scene assumption in geometric image reconstruction.
Isolating Sources of Disentanglement in Variational Autoencoders
Tian Qi Chen, Xuechen Li, Roger B. Grosse, David K. Duvenaud
Neural Information Processing Systems http://nips.cc/
Neural 4D Hand Representation Using Fourier Query Flow
Thus, they do not effectively capture implicit correspondences between articulated shapes or regularize jittery temporal deformations.
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
Zeyuan Allen-Zhu, Yuanzhi Li, Yingyu Liang
The fundamental learning theory behind neural networks remains largely open. What classes of functions can neural networks actually learn?
Practical Equivariances via Relational Conditional Neural Processes Daolang Huang 1 Manuel Haussmann
However, prior attempts to include equivariances in CNPs do not scale effectively beyond two input dimensions.