2 citations · 3 across the 4 of their papers we have counts for
7 papers
ETA: Energy-based Test-time Adaptation for Depth Completion
Younjoon Chung, Hyoungseob Park, Patrick Rim +7
We propose a method for test-time adaptation of pretrained depth completion models. Depth completion models, trained on some ``source'' data, often predict erroneous outputs when t…
Small Lesion Segmentation in Brain MRIs with Subpixel Embedding
Alex Wong, Allison Chen, Yangchao Wu +4
We present a method to segment MRI scans of the human brain into ischemic stroke lesion and normal tissues. We propose a neural network architecture in the form of a standard encod…
Learning Topology from Synthetic Data for Unsupervised Depth Completion
Alex Wong, Safa Cicek, Stefano Soatto
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…
Targeted Adversarial Perturbations for Monocular Depth Prediction
Alex Wong, Safa Cicek, Stefano Soatto
We study the effect of adversarial perturbations on the task of monocular depth prediction. Specifically, we explore the ability of small, imperceptible additive perturbations to s…
Unsupervised Domain Adaptation via Regularized Conditional Alignment
Safa Cicek, Stefano Soatto
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 classifi…
Input and Weight Space Smoothing for Semi-supervised Learning
Safa Cicek, Stefano Soatto
We propose regularizing the empirical loss for semi-supervised learning by acting on both the input (data) space, and the weight (parameter) space. We show that the two are not equ…
SaaS: Speed as a Supervisor for Semi-supervised Learning
Safa Cicek, Alhussein Fawzi, Stefano Soatto
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 ite…