Search Results for author:
Found 6 papers, 4 papers with code
Small Lesion Segmentation in Brain MRIs with Subpixel Embedding
We propose a neural network architecture in the form of a standard encoder-decoder where predictions are guided by a spatial expansion embedding network.
Learning Topology from Synthetic Data for Unsupervised Depth Completion
We present a method for inferring dense depth maps from images and sparse depth measurements by leveraging synthetic data to learn the association of sparse point clouds with dense natural shapes, and using the image as evidence to validate the predicted depth map.
Ranked #3 on
Depth Completion
on VOID
Targeted Adversarial Perturbations for Monocular Depth Prediction
We study the effect of adversarial perturbations on the task of monocular depth prediction.
Unsupervised Domain Adaptation via Regularized Conditional Alignment
We propose a method for unsupervised domain adaptation that trains a shared embedding to align the joint distributions of inputs (domain) and outputs (classes), making any classifier agnostic to the domain.
Input and Weight Space Smoothing for Semi-supervised Learning
We propose regularizing the empirical loss for semi-supervised learning by acting on both the input (data) space, and the weight (parameter) space.
SaaS: Speed as a Supervisor for Semi-supervised Learning
We introduce the SaaS Algorithm for semi-supervised learning, which uses learning speed during stochastic gradient descent in a deep neural network to measure the quality of an iterative estimate of the posterior probability of unknown labels.
