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Found 6 papers, 2 papers with code
NVAutoNet: Fast and Accurate 360$^{\circ}$ 3D Visual Perception For Self Driving
Achieving robust and real-time 3D perception is fundamental for autonomous vehicles.
Appearance invariance in convolutional networks with neighborhood similarity
We present a neighborhood similarity layer (NSL) which induces appearance invariance in a network when used in conjunction with convolutional layers.
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning
Effective convolutional neural networks are trained on large sets of labeled data.
Ranked #14 on
Semi-Supervised Image Classification
on SVHN, 250 Labels
Mutual Exclusivity Loss for Semi-Supervised Deep Learning
In this paper we consider the problem of semi-supervised learning with deep Convolutional Neural Networks (ConvNets).
Unsupervised Total Variation Loss for Semi-supervised Deep Learning of Semantic Segmentation
This can be seen as a regularization term that promotes piecewise smoothness of the label probability vector image produced by the ConvNet during learning.
Disjunctive Normal Networks
Backpropagation, which is used to train artificial neural networks, suffers from the herd effect problem which leads to long training times and limit classification accuracy.
